Bio
Dr. Shirin Abbasinejad Enger is a tenured Associate Professor at the Medical Physics Unit, Gerald Bronfman Department of Oncology, Director at the Medical Physics Unit and holder of a Tier 2 Canada Research Chair in Medical Physics. She is research director in Translational Physics and Radiobiology at the Lady Davis Institute and Segal Cancer Centre of the Jewish General Hospital.
Dr. Abbasinejad Enger received her Ph.D. degree from Uppsala University in 2009 and was a postdoctoral fellow at Université Laval from 2009 to 2011. She has several leadership roles in international and national workgroups and committees. She is chair of the Intensity Modulated and Anisotropic Brachytherapy Sources Task Group (TG-337) and member of multiple other work and task groups at the American Association of Physicists in Medicine. This includes the workgroup on Science Council Equity, Diversity and Inclusion. She is member of the Science Committee and Women’s Committee at the Canadian Association of Medical Physicists.
Dr. Abbasinejad Enger’s multidisciplinary research involves development of applied technology to address current limitations in radiotherapy treatment and imaging of cancer. She has multiple patents and has received competitive funding to develop her innovations from proof of concept to clinical trials. Please have a look at her four main research axes; development of novel brachytherapy technology, radiobiology and microdosimetry, detector development, and artificial intelligence.
Her research group has won several medical innovation awards and several papers published by her group have been selected as editor’s pick in major peer-reviewed journals in the field.
2022
Zou, Yujing; Lecavalier-Barsoum, Magali; Pelmus, Manuela; Maleki, Farhad; Enger, Shirin A.
Curietherapies 2022.
@conference{nokey,
title = {Predictive modeling of post radiation-therapy recurrence for gynecological cancer patients using clinical and histopathology imaging features},
author = {Yujing Zou and Magali Lecavalier-Barsoum and Manuela Pelmus and Farhad Maleki and Shirin A. Enger},
url = {https://www.researchgate.net/publication/361436138_Predictive_modeling_of_post_radiation-therapy_recurrence_for_gynecological_cancer_patients_using_clinical_and_histopathology_imaging_features},
year = {2022},
date = {2022-05-23},
urldate = {2022-05-23},
organization = {Curietherapies},
abstract = {Purpose: To build a machine-learning (ML) classifier to predict the clinical endpoint of post-Radiation-Therapy (RT) recurrence of gynecological cancer patients, while exploring the outcome predictability of cell spacing and nuclei size pre-treatment histopathology image features and clinical variables. Materials and Methods: Thirty-six gynecological (i.e., cervix, vaginal, and vulva) cancer patients (median age at diagnosis = 59.5 years) with a median follow-up time of 25.7 months, nine of which (event rate of 25%) experienced post-RT recurrence, were included in this analysis. Patient-specific nuclei size and cell spacing distributions from cancerous and non-tumoral regions of pre-treatment hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) were extracted. The mean and standard deviation of these distributions were computed as imaging features for each WSI. Clinical features of clinical and radiological stage at the time of radiation, p16 status, age at diagnosis, and cancer type were also obtained. Uniquely, a Tree-based Pipeline Optimization Tool (TPOT) AutoML approach, including hyperparameter tuning, was implemented to find the best performing pipeline for this class-imbalanced and small dataset. A Radial Basis Function Kernel (RBF) sampler (gamma = 0.25) was applied to combined imaging and clinical input variables for training. The resulting features were fed into an XGBoost (ie., eXtreme gradient-boosting) classifier (learning rate = 0.1). Its outputs were propagated as “synthetic features” followed by polynomial feature transforms. All raw and transformed features were trained with a decision tree classification algorithm. Results of model evaluation metrics from a 10-fold stratified shuffle split cross-validation were averaged. A permutation test (n=1000) was performed to validate the significance of the classification scores. Results: Our model achieved a 10-fold stratified shuffle split cross-validation scores of 0.87 for mean accuracy, 0.92 for mean balanced accuracy, 0.78 for precision, 1 for recall, 0.85 for F1 score, and 0.92 for Area Under the Curve of Receiver Operating Characteristics Curve, to predict our patient cohort’s post-RT recurrence binary outcome. A p-value of 0.036 was obtained from the permutation test. This implies real dependencies between our combined imaging and clinical features and outcomes which were learned by the classifier, and the primising model performance was not by chance. Conclusions: Despite the small dataset and low event rate, as a proof of concept, we showed that a decision-tree-based ML classification algorithm using an XGBoost algorithm is able to utilize combined (cell spacing & nuclei size) imaging and clinical features to predict post-RT outcomes for gynecological cancer patients.},
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pubstate = {published},
tppubtype = {conference}
}
Purpose: To build a machine-learning (ML) classifier to predict the clinical endpoint of post-Radiation-Therapy (RT) recurrence of gynecological cancer patients, while exploring the outcome predictability of cell spacing and nuclei size pre-treatment histopathology image features and clinical variables. Materials and Methods: Thirty-six gynecological (i.e., cervix, vaginal, and vulva) cancer patients (median age at diagnosis = 59.5 years) with a median follow-up time of 25.7 months, nine of which (event rate of 25%) experienced post-RT recurrence, were included in this analysis. Patient-specific nuclei size and cell spacing distributions from cancerous and non-tumoral regions of pre-treatment hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) were extracted. The mean and standard deviation of these distributions were computed as imaging features for each WSI. Clinical features of clinical and radiological stage at the time of radiation, p16 status, age at diagnosis, and cancer type were also obtained. Uniquely, a Tree-based Pipeline Optimization Tool (TPOT) AutoML approach, including hyperparameter tuning, was implemented to find the best performing pipeline for this class-imbalanced and small dataset. A Radial Basis Function Kernel (RBF) sampler (gamma = 0.25) was applied to combined imaging and clinical input variables for training. The resulting features were fed into an XGBoost (ie., eXtreme gradient-boosting) classifier (learning rate = 0.1). Its outputs were propagated as “synthetic features” followed by polynomial feature transforms. All raw and transformed features were trained with a decision tree classification algorithm. Results of model evaluation metrics from a 10-fold stratified shuffle split cross-validation were averaged. A permutation test (n=1000) was performed to validate the significance of the classification scores. Results: Our model achieved a 10-fold stratified shuffle split cross-validation scores of 0.87 for mean accuracy, 0.92 for mean balanced accuracy, 0.78 for precision, 1 for recall, 0.85 for F1 score, and 0.92 for Area Under the Curve of Receiver Operating Characteristics Curve, to predict our patient cohort’s post-RT recurrence binary outcome. A p-value of 0.036 was obtained from the permutation test. This implies real dependencies between our combined imaging and clinical features and outcomes which were learned by the classifier, and the primising model performance was not by chance. Conclusions: Despite the small dataset and low event rate, as a proof of concept, we showed that a decision-tree-based ML classification algorithm using an XGBoost algorithm is able to utilize combined (cell spacing & nuclei size) imaging and clinical features to predict post-RT outcomes for gynecological cancer patients.
Zou, Yujing; Lecavalier-Barsoum, Magali; Pelmus, Manuela; Maleki, Farhad; Enger, Shirin A.
Young Investigator Competition Winner at the Curietherapies Conference award
2022.
@award{nokey,
title = {Young Investigator Competition Winner at the Curietherapies Conference },
author = {Yujing Zou and Magali Lecavalier-Barsoum and Manuela Pelmus and Farhad Maleki and Shirin A. Enger },
url = {https://www.researchgate.net/publication/360979157_SP-0014_McMedHacks_Deep_learning_for_medical_image_analysis_workshops_and_Hackathon_in_radiation_oncology},
year = {2022},
date = {2022-05-23},
urldate = {2022-05-23},
organization = {Curietherapies},
abstract = {Purpose/Objective: The McMedHacks workshop and presentation series was created to teach individuals from various backgrounds about deep learning (DL) for medical image analysis in May, 2021. Material/Methods: McMedHacks is a free and student-led 8-week summer program. Registration for the event was open to everyone, including a form to survey participants’ area of expertise, country of origin, level of study, and level of programming skills. The weekly workshops were instructed by 8 students and experts assisted by 20 mentors who provided weekly tutorials. Recent developments in DL and medical physics were highlighted by 21 leaders from industry and academia. A virtual grand challenge Hackathon took place at the end of the workshop series. All events were held virtually and recorded on Zoom to accommodate all time zones and locations. The workshops were designed as interactive coding demos and shared through Google Colab notebooks. Results: McMedHacks gained 356 registrations from participants of 38 different countries (Fig. 1) from undergraduates, to PhDs and MDs. A vast number of disciplines and professions were represented, dominated by medical physics students, academic, and clinical medical physicists (Fig. 2). Sixty-nine participants earned a certificate of completion by having engaged with at least 12 of all 14 events. The program received participant feedback average scores of 4.768, 4.478, 4.579, 4.292, 4.84 out of five for the qualities of presentation, workshop session, tutorial and mentor, assignments, and course delivery, respectively. The eight-week long workshop’s duration allowed participants to digest the taught materials in a continuous manner as opposed to bootcamp-style conference workshops. Conclusion: The overwhelming interest and engagement for the McMedHacks workshop series from the Radiation Oncology (RadOnc) community illustrates a demand for Artificial Intelligence (AI) education in RadOnc. The future of RadOnc clinics will inevitably integrate AI. Therefore, current RadOnc professionals, and student and resident trainees should be prepared to understand basic AI principles and its applications to troubleshoot, innovate, and collaborate. McMedHacks set an excellent example of promoting open and multidisciplinary education, scientific communication, and leadership for integrating AI education into the RadOnc community on an international level. Therefore, we advocate for implementation of AI curriculums in professional education programs such as Commission on Accreditation of Medical Physics Education Programs (CAMPEP). Furthermore, we encourage experts from around the world in the field of AI, or RadOnc, or both, to take initiatives like McMedHacks to collaborate and push forward AI education in their departments and lead practical workshops, regardless of their levels of education.},
keywords = {},
pubstate = {published},
tppubtype = {award}
}
Purpose/Objective: The McMedHacks workshop and presentation series was created to teach individuals from various backgrounds about deep learning (DL) for medical image analysis in May, 2021. Material/Methods: McMedHacks is a free and student-led 8-week summer program. Registration for the event was open to everyone, including a form to survey participants’ area of expertise, country of origin, level of study, and level of programming skills. The weekly workshops were instructed by 8 students and experts assisted by 20 mentors who provided weekly tutorials. Recent developments in DL and medical physics were highlighted by 21 leaders from industry and academia. A virtual grand challenge Hackathon took place at the end of the workshop series. All events were held virtually and recorded on Zoom to accommodate all time zones and locations. The workshops were designed as interactive coding demos and shared through Google Colab notebooks. Results: McMedHacks gained 356 registrations from participants of 38 different countries (Fig. 1) from undergraduates, to PhDs and MDs. A vast number of disciplines and professions were represented, dominated by medical physics students, academic, and clinical medical physicists (Fig. 2). Sixty-nine participants earned a certificate of completion by having engaged with at least 12 of all 14 events. The program received participant feedback average scores of 4.768, 4.478, 4.579, 4.292, 4.84 out of five for the qualities of presentation, workshop session, tutorial and mentor, assignments, and course delivery, respectively. The eight-week long workshop’s duration allowed participants to digest the taught materials in a continuous manner as opposed to bootcamp-style conference workshops. Conclusion: The overwhelming interest and engagement for the McMedHacks workshop series from the Radiation Oncology (RadOnc) community illustrates a demand for Artificial Intelligence (AI) education in RadOnc. The future of RadOnc clinics will inevitably integrate AI. Therefore, current RadOnc professionals, and student and resident trainees should be prepared to understand basic AI principles and its applications to troubleshoot, innovate, and collaborate. McMedHacks set an excellent example of promoting open and multidisciplinary education, scientific communication, and leadership for integrating AI education into the RadOnc community on an international level. Therefore, we advocate for implementation of AI curriculums in professional education programs such as Commission on Accreditation of Medical Physics Education Programs (CAMPEP). Furthermore, we encourage experts from around the world in the field of AI, or RadOnc, or both, to take initiatives like McMedHacks to collaborate and push forward AI education in their departments and lead practical workshops, regardless of their levels of education.
Enger, Shirin A.; Famulari, Gabriel
Delivery system for intensity modulated high dose rate brachytherapy with intermediate energy brachytherapy isotopes Patent
2022, (US Patent 11,324,966).
@patent{enger2022delivery,
title = {Delivery system for intensity modulated high dose rate brachytherapy with intermediate energy brachytherapy isotopes},
author = {Shirin A. Enger and Gabriel Famulari},
year = {2022},
date = {2022-05-01},
urldate = {2022-05-01},
publisher = {Google Patents},
note = {US Patent 11,324,966},
keywords = {},
pubstate = {published},
tppubtype = {patent}
}
Carroll, Liam; Enger, Shirin A.
Second prize at the International Conference on Monte Carlo Techniques for Medical Applications award
2022.
@award{nokey,
title = {Second prize at the International Conference on Monte Carlo Techniques for Medical Applications },
author = {Liam Carroll and Shirin A. Enger},
year = {2022},
date = {2022-04-13},
urldate = {2022-04-13},
journal = {International Conference on Monte Carlo Techniques for Medical Applications },
abstract = {Introduction
Geant4[1] is a Monte Carlo toolkit that provides a flexible platform to design radiation transport simulations. This flexibility requires a high level of complexity when writing new user-codes. New users must undergo extensive training to begin writing useful simulations. The aim of this study was to develop a modular radiation simulation software package called MaRSS based on Geant4 user-code to serve as both an educational tool and as a simulation tool for medical radiation detector simulations.
Materials & Methods
MaRSS builds on Geant4 using Penelope electromagnetic physics models and cross-sections[2]. To give the users possibility to change simulation parameters without changing the source code, MaRSS is equipped with a set of messenger classes which are intercom modules provided by Geant4 to configure applications and provide user interactivity with the code. These messenger classes add additional user commands that can be used to add or remove volumes from the simulation geometry and associated sensitive detectors. The sensitive detectors are objects that Geant4 uses to save simulation results. A number of default volumes and detectors are included in MaRSS that can be used for the design of scintillating fiber-based radiation detectors. MaRSS adds to the existing Geant4 sensitive detector code by creating a new base class called RunSD that is inherited by all sensitive detector objects. RunSD is implemented such that the code needed to initialize a sensitive detector is included in two classes, normally, this code is spread out in several classes. A similar approach is taken with geometrical volumes. To validate MaRSS, range in water of positrons emitted from four radioisotopes commonly used for positron emission tomography was calculated and compared with published work: Fluorine-18 (18F), Carbon-11 (11C), Oxygen-15 (15O) and Gallium-68 (68Ga). A sphere with a radius of 1 m was filed with water. For each radioisotope, 100 million decay events were simulated originating at the center of the simulated water sphere. The resulting positrons were allowed to annihilate. Two energy cuts were simulated, 1 keV and 0.1 keV. Two histograms were created, one using the annihilation locations and another with the energy of the emitted positrons. Results were compared with published positron range values calculated with a PENELOPE[2]-based Monte Carlo software called PeneloPET [3], an analytical expression to estimate the range of positrons described by Cal-Gonzales et.al.[3], a simulation by Lehnert et. al. [4] written using GATE[5] and a separate Monte Carlo software written by Champion and Le Loirec[6] that directly simulates the formation of positronium in water to calculate positron range.
Results
Figure 1 shows the mean calculated positron ranges and calculated positron emission energies compared to literature values. Simulated positron energy means were within 1.8% of literature values. Simulated ranges were within 2% of GATE simulation[4].
References
[1] S. Agostinelli et al., “Geant4—a simulation toolkit,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 506, no. 3, pp. 250–303, Jul. 2003.
[2] J. Baró, J. Sempau, J. M. Fernández-Varea, and F. Salvat, “PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter,” Nucl. Inst. Methods Phys. Res. B, vol. 100, no. 1, pp. 31–46, May 1995.
[3] J. Cal-González et al., “Positron range estimations with PeneloPET,” Phys. Med. Biol., vol. 58, no. 15, pp. 5127–5152, 2013.
[4] W. Lehnert, M.-C. Gregoire, A. Reilhac, and S. R. Meikle, “Analytical positron range modelling in heterogeneous media for PET Monte Carlo simulation,” Phys. Med. Biol., vol. 56, no. 11, p. 3313, May 2011.
[5] D. Strul, G. Santin, D. Lazaro, V. Breton, and C. Morel, “GATE (geant4 application for tomographic emission): a PET/SPECT general-purpose simulation platform,” Nucl. Phys. B - Proc. Suppl., vol. 125, pp. 75–79, Sep. 2003.
[6] C. Champion and C. Le Loirec, “Positron follow-up in liquid water: II. Spatial and energetic study for the most important radioisotopes used in PET,” Phys. Med. Biol., vol. 52, no. 22, pp. 6605–6625, Nov. 2007.
},
keywords = {},
pubstate = {published},
tppubtype = {award}
}
Introduction
Geant4[1] is a Monte Carlo toolkit that provides a flexible platform to design radiation transport simulations. This flexibility requires a high level of complexity when writing new user-codes. New users must undergo extensive training to begin writing useful simulations. The aim of this study was to develop a modular radiation simulation software package called MaRSS based on Geant4 user-code to serve as both an educational tool and as a simulation tool for medical radiation detector simulations.
Materials & Methods
MaRSS builds on Geant4 using Penelope electromagnetic physics models and cross-sections[2]. To give the users possibility to change simulation parameters without changing the source code, MaRSS is equipped with a set of messenger classes which are intercom modules provided by Geant4 to configure applications and provide user interactivity with the code. These messenger classes add additional user commands that can be used to add or remove volumes from the simulation geometry and associated sensitive detectors. The sensitive detectors are objects that Geant4 uses to save simulation results. A number of default volumes and detectors are included in MaRSS that can be used for the design of scintillating fiber-based radiation detectors. MaRSS adds to the existing Geant4 sensitive detector code by creating a new base class called RunSD that is inherited by all sensitive detector objects. RunSD is implemented such that the code needed to initialize a sensitive detector is included in two classes, normally, this code is spread out in several classes. A similar approach is taken with geometrical volumes. To validate MaRSS, range in water of positrons emitted from four radioisotopes commonly used for positron emission tomography was calculated and compared with published work: Fluorine-18 (18F), Carbon-11 (11C), Oxygen-15 (15O) and Gallium-68 (68Ga). A sphere with a radius of 1 m was filed with water. For each radioisotope, 100 million decay events were simulated originating at the center of the simulated water sphere. The resulting positrons were allowed to annihilate. Two energy cuts were simulated, 1 keV and 0.1 keV. Two histograms were created, one using the annihilation locations and another with the energy of the emitted positrons. Results were compared with published positron range values calculated with a PENELOPE[2]-based Monte Carlo software called PeneloPET [3], an analytical expression to estimate the range of positrons described by Cal-Gonzales et.al.[3], a simulation by Lehnert et. al. [4] written using GATE[5] and a separate Monte Carlo software written by Champion and Le Loirec[6] that directly simulates the formation of positronium in water to calculate positron range.
Results
Figure 1 shows the mean calculated positron ranges and calculated positron emission energies compared to literature values. Simulated positron energy means were within 1.8% of literature values. Simulated ranges were within 2% of GATE simulation[4].
References
[1] S. Agostinelli et al., “Geant4—a simulation toolkit,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 506, no. 3, pp. 250–303, Jul. 2003.
[2] J. Baró, J. Sempau, J. M. Fernández-Varea, and F. Salvat, “PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter,” Nucl. Inst. Methods Phys. Res. B, vol. 100, no. 1, pp. 31–46, May 1995.
[3] J. Cal-González et al., “Positron range estimations with PeneloPET,” Phys. Med. Biol., vol. 58, no. 15, pp. 5127–5152, 2013.
[4] W. Lehnert, M.-C. Gregoire, A. Reilhac, and S. R. Meikle, “Analytical positron range modelling in heterogeneous media for PET Monte Carlo simulation,” Phys. Med. Biol., vol. 56, no. 11, p. 3313, May 2011.
[5] D. Strul, G. Santin, D. Lazaro, V. Breton, and C. Morel, “GATE (geant4 application for tomographic emission): a PET/SPECT general-purpose simulation platform,” Nucl. Phys. B - Proc. Suppl., vol. 125, pp. 75–79, Sep. 2003.
[6] C. Champion and C. Le Loirec, “Positron follow-up in liquid water: II. Spatial and energetic study for the most important radioisotopes used in PET,” Phys. Med. Biol., vol. 52, no. 22, pp. 6605–6625, Nov. 2007.
Geant4[1] is a Monte Carlo toolkit that provides a flexible platform to design radiation transport simulations. This flexibility requires a high level of complexity when writing new user-codes. New users must undergo extensive training to begin writing useful simulations. The aim of this study was to develop a modular radiation simulation software package called MaRSS based on Geant4 user-code to serve as both an educational tool and as a simulation tool for medical radiation detector simulations.
Materials & Methods
MaRSS builds on Geant4 using Penelope electromagnetic physics models and cross-sections[2]. To give the users possibility to change simulation parameters without changing the source code, MaRSS is equipped with a set of messenger classes which are intercom modules provided by Geant4 to configure applications and provide user interactivity with the code. These messenger classes add additional user commands that can be used to add or remove volumes from the simulation geometry and associated sensitive detectors. The sensitive detectors are objects that Geant4 uses to save simulation results. A number of default volumes and detectors are included in MaRSS that can be used for the design of scintillating fiber-based radiation detectors. MaRSS adds to the existing Geant4 sensitive detector code by creating a new base class called RunSD that is inherited by all sensitive detector objects. RunSD is implemented such that the code needed to initialize a sensitive detector is included in two classes, normally, this code is spread out in several classes. A similar approach is taken with geometrical volumes. To validate MaRSS, range in water of positrons emitted from four radioisotopes commonly used for positron emission tomography was calculated and compared with published work: Fluorine-18 (18F), Carbon-11 (11C), Oxygen-15 (15O) and Gallium-68 (68Ga). A sphere with a radius of 1 m was filed with water. For each radioisotope, 100 million decay events were simulated originating at the center of the simulated water sphere. The resulting positrons were allowed to annihilate. Two energy cuts were simulated, 1 keV and 0.1 keV. Two histograms were created, one using the annihilation locations and another with the energy of the emitted positrons. Results were compared with published positron range values calculated with a PENELOPE[2]-based Monte Carlo software called PeneloPET [3], an analytical expression to estimate the range of positrons described by Cal-Gonzales et.al.[3], a simulation by Lehnert et. al. [4] written using GATE[5] and a separate Monte Carlo software written by Champion and Le Loirec[6] that directly simulates the formation of positronium in water to calculate positron range.
Results
Figure 1 shows the mean calculated positron ranges and calculated positron emission energies compared to literature values. Simulated positron energy means were within 1.8% of literature values. Simulated ranges were within 2% of GATE simulation[4].
References
[1] S. Agostinelli et al., “Geant4—a simulation toolkit,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 506, no. 3, pp. 250–303, Jul. 2003.
[2] J. Baró, J. Sempau, J. M. Fernández-Varea, and F. Salvat, “PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter,” Nucl. Inst. Methods Phys. Res. B, vol. 100, no. 1, pp. 31–46, May 1995.
[3] J. Cal-González et al., “Positron range estimations with PeneloPET,” Phys. Med. Biol., vol. 58, no. 15, pp. 5127–5152, 2013.
[4] W. Lehnert, M.-C. Gregoire, A. Reilhac, and S. R. Meikle, “Analytical positron range modelling in heterogeneous media for PET Monte Carlo simulation,” Phys. Med. Biol., vol. 56, no. 11, p. 3313, May 2011.
[5] D. Strul, G. Santin, D. Lazaro, V. Breton, and C. Morel, “GATE (geant4 application for tomographic emission): a PET/SPECT general-purpose simulation platform,” Nucl. Phys. B - Proc. Suppl., vol. 125, pp. 75–79, Sep. 2003.
[6] C. Champion and C. Le Loirec, “Positron follow-up in liquid water: II. Spatial and energetic study for the most important radioisotopes used in PET,” Phys. Med. Biol., vol. 52, no. 22, pp. 6605–6625, Nov. 2007.
Rahbaran, Maryam; Kalinowski, Jonathan; Tsui, James Man Git; DeCunha, Joseph; Enger, Shirin A.
Monte-Carlo Based Simulations of the Uncertainties in Clinical Water-Based Intravascular Brachytherapy Dosimetry Presentation
11.04.2022.
@misc{nokey,
title = {Monte-Carlo Based Simulations of the Uncertainties in Clinical Water-Based Intravascular Brachytherapy Dosimetry},
author = {Maryam Rahbaran and Jonathan Kalinowski and James Man Git Tsui and Joseph DeCunha and Shirin A. Enger},
year = {2022},
date = {2022-04-11},
urldate = {2022-04-11},
journal = {MCMA},
abstract = {"Introduction
Coronary artery disease (CAD) is the most common form of cardiovascular disease and is caused by excess plaque along the arterial wall, blocking blood flow to the heart (stenosis). Percutaneous transluminal coronary angioplasty widens a narrowed artery, leaving behind metal stents (1). However, in-stent restenosis (ISR) may occur due to damage to the arterial wall tissue, triggering neointimal hyperplasia which produces fibrotic and calcified plaques, narrowing the artery again. Drug-eluting stents (DES) slowly release medication to inhibit neointimal hyperplasia to prevent ISR but they fail in 3% to 20% of cases (2). Intravascular brachytherapy (IVBT), which uses b-emitting radionuclides to prevent ISR, is used in these failed cases. However, current dosimetry for IVBT is water based and does not consider attenuation of the radiation by heterogeneities such as the IVBT device guidewire, non-uniform distribution of calcified plaques, and stent material, or the angular dependence of dose distribution (3, 4, 5). The aim of this study was to investigate the uncertainties in clinical water based IVBT dosimetry, considering the effect of heterogeneities on dose distribution.
Materials & Methods
An inhouse Monte-Carlo based dosimetry package for IVBT applications based on Geant4 10.04 (patch 2) was developed. Patient’s artery was modelled as a 32 mm long, 8.4 mm diameter cylinder comprised of three layers: tunica media, represented with muscle, tunica intima, represented with fibrotic plaque, and tunica adventitia, represented with collagen. These layers had mass densities 1.06 g/cm3, 1.22 g/cm3 and 1.07 g/cm3 respectively. The innermost layer consisted of calcified plaque of density 1.45 g/cm3 with varying thicknesses between 0.9 and 1.9 mm with an eccentric shape and a rough surface. The stents had similar composition to Boston Scientific Synergy stents and were modelled to not overlap. The Novoste Beta-Cath 3.5F IVBT device model was used, which has a 90Sr90Y source. The geometry is shown in Figure 1a. A cylindrical scoring geometry was implemented. Two set of simulations were performed. In the first simulation called water phantom, the entire system consisted of water with unit density, and dose to water was calculated similar to the clinical water based dosimetry. In the second simulation called the artery model proper material and mass densities were assigned to each component. To ensure uncertainties below 0.8% within a 1 mm radial distance to the source and 2% within 4.2 mm from the source, 100 million decay events were simulated. The Penelope physics list was used to simulate the electromagnetic interactions between particles. Average, minimum, and maximum dose was calculated at 2.0 mm from the source center and directly and 1 mm behind the outermost stents and guidewire. Absorbed dose was normalized to 23 Gy at 2.0 mm from the source center.
Results
International Conference on Monte Carlo Techniques for Medical Applications, 2022
Compared to the water phantom (Figure 1b), average dose in the artery model (Figure 1c) was attenuated by 50.9% at 2 mm from the source centre and directly behind the guidewire and outermost stent by 66.2%, and by 69.5% 1 mm behind this region. There was significant variation in dose around the source due to the guidewire attenuating dose the most, and heterogeneous distribution of calcification.
Discussion & Conclusions
Dosimetry for IVBT based on dose rate in water is not accurate. Heterogeneities need to be considered to deliver adequate dose to the lesion area. Stent material, heterogenous distribution of calcification and the off cantered placement of the guidewire affects the uniformity of dose distribution around the source. Patients may benefit from personalized treatment planning taking dose-attenuating by different tissue/material heterogeneities into account.
References
[1] Virani, Salim, S., et al. ""Heart Disease and Stroke Statistics—2020 Update"". Circulation, vol. 141, no. 9, March 03, 2020, pp. e336. doi: 10.1161/CIR.0000000000000757.
[2] Lee M, Banka G. In-stent restenosis. Interv Cardiol Clin 2016;5: 211e220.
[3] Chiu-Tsao ST, Schaart DR, Soares CG, et al. Dose calculation formalisms and consensus dosimetry parameters for intravascular brachytherapy dosimetry: Recommendations of the AAPM Therapy Physics Committee Task Group No. 149. Med Phys 2007;34: 4126e4157.
[4] Rivard MJ, Coursey BM, DeWerd LA, et al. Update of AAPM Task Group No. 43 Report: A revised AAPM protocol for brachytherapy dose calculations. Med Phys 2004;31:633e674.
[5] Nath R, Amols H, Coffey C, et al. Intravascular brachytherapy physics: Report of the AAPM Radiation Therapy Committee Task group No. 60. Med Phys 1999;26:119e152.’
[6] Agostinelli S, Allison J, Amako K, et al. Geant4da simulation toolkit. Nucl Instrum Methods Phys Res 2003;506:230e303."},
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tppubtype = {presentation}
}
"Introduction
Coronary artery disease (CAD) is the most common form of cardiovascular disease and is caused by excess plaque along the arterial wall, blocking blood flow to the heart (stenosis). Percutaneous transluminal coronary angioplasty widens a narrowed artery, leaving behind metal stents (1). However, in-stent restenosis (ISR) may occur due to damage to the arterial wall tissue, triggering neointimal hyperplasia which produces fibrotic and calcified plaques, narrowing the artery again. Drug-eluting stents (DES) slowly release medication to inhibit neointimal hyperplasia to prevent ISR but they fail in 3% to 20% of cases (2). Intravascular brachytherapy (IVBT), which uses b-emitting radionuclides to prevent ISR, is used in these failed cases. However, current dosimetry for IVBT is water based and does not consider attenuation of the radiation by heterogeneities such as the IVBT device guidewire, non-uniform distribution of calcified plaques, and stent material, or the angular dependence of dose distribution (3, 4, 5). The aim of this study was to investigate the uncertainties in clinical water based IVBT dosimetry, considering the effect of heterogeneities on dose distribution.
Materials & Methods
An inhouse Monte-Carlo based dosimetry package for IVBT applications based on Geant4 10.04 (patch 2) was developed. Patient’s artery was modelled as a 32 mm long, 8.4 mm diameter cylinder comprised of three layers: tunica media, represented with muscle, tunica intima, represented with fibrotic plaque, and tunica adventitia, represented with collagen. These layers had mass densities 1.06 g/cm3, 1.22 g/cm3 and 1.07 g/cm3 respectively. The innermost layer consisted of calcified plaque of density 1.45 g/cm3 with varying thicknesses between 0.9 and 1.9 mm with an eccentric shape and a rough surface. The stents had similar composition to Boston Scientific Synergy stents and were modelled to not overlap. The Novoste Beta-Cath 3.5F IVBT device model was used, which has a 90Sr90Y source. The geometry is shown in Figure 1a. A cylindrical scoring geometry was implemented. Two set of simulations were performed. In the first simulation called water phantom, the entire system consisted of water with unit density, and dose to water was calculated similar to the clinical water based dosimetry. In the second simulation called the artery model proper material and mass densities were assigned to each component. To ensure uncertainties below 0.8% within a 1 mm radial distance to the source and 2% within 4.2 mm from the source, 100 million decay events were simulated. The Penelope physics list was used to simulate the electromagnetic interactions between particles. Average, minimum, and maximum dose was calculated at 2.0 mm from the source center and directly and 1 mm behind the outermost stents and guidewire. Absorbed dose was normalized to 23 Gy at 2.0 mm from the source center.
Results
International Conference on Monte Carlo Techniques for Medical Applications, 2022
Compared to the water phantom (Figure 1b), average dose in the artery model (Figure 1c) was attenuated by 50.9% at 2 mm from the source centre and directly behind the guidewire and outermost stent by 66.2%, and by 69.5% 1 mm behind this region. There was significant variation in dose around the source due to the guidewire attenuating dose the most, and heterogeneous distribution of calcification.
Discussion & Conclusions
Dosimetry for IVBT based on dose rate in water is not accurate. Heterogeneities need to be considered to deliver adequate dose to the lesion area. Stent material, heterogenous distribution of calcification and the off cantered placement of the guidewire affects the uniformity of dose distribution around the source. Patients may benefit from personalized treatment planning taking dose-attenuating by different tissue/material heterogeneities into account.
References
[1] Virani, Salim, S., et al. ""Heart Disease and Stroke Statistics—2020 Update"". Circulation, vol. 141, no. 9, March 03, 2020, pp. e336. doi: 10.1161/CIR.0000000000000757.
[2] Lee M, Banka G. In-stent restenosis. Interv Cardiol Clin 2016;5: 211e220.
[3] Chiu-Tsao ST, Schaart DR, Soares CG, et al. Dose calculation formalisms and consensus dosimetry parameters for intravascular brachytherapy dosimetry: Recommendations of the AAPM Therapy Physics Committee Task Group No. 149. Med Phys 2007;34: 4126e4157.
[4] Rivard MJ, Coursey BM, DeWerd LA, et al. Update of AAPM Task Group No. 43 Report: A revised AAPM protocol for brachytherapy dose calculations. Med Phys 2004;31:633e674.
[5] Nath R, Amols H, Coffey C, et al. Intravascular brachytherapy physics: Report of the AAPM Radiation Therapy Committee Task group No. 60. Med Phys 1999;26:119e152.’
[6] Agostinelli S, Allison J, Amako K, et al. Geant4da simulation toolkit. Nucl Instrum Methods Phys Res 2003;506:230e303."
Coronary artery disease (CAD) is the most common form of cardiovascular disease and is caused by excess plaque along the arterial wall, blocking blood flow to the heart (stenosis). Percutaneous transluminal coronary angioplasty widens a narrowed artery, leaving behind metal stents (1). However, in-stent restenosis (ISR) may occur due to damage to the arterial wall tissue, triggering neointimal hyperplasia which produces fibrotic and calcified plaques, narrowing the artery again. Drug-eluting stents (DES) slowly release medication to inhibit neointimal hyperplasia to prevent ISR but they fail in 3% to 20% of cases (2). Intravascular brachytherapy (IVBT), which uses b-emitting radionuclides to prevent ISR, is used in these failed cases. However, current dosimetry for IVBT is water based and does not consider attenuation of the radiation by heterogeneities such as the IVBT device guidewire, non-uniform distribution of calcified plaques, and stent material, or the angular dependence of dose distribution (3, 4, 5). The aim of this study was to investigate the uncertainties in clinical water based IVBT dosimetry, considering the effect of heterogeneities on dose distribution.
Materials & Methods
An inhouse Monte-Carlo based dosimetry package for IVBT applications based on Geant4 10.04 (patch 2) was developed. Patient’s artery was modelled as a 32 mm long, 8.4 mm diameter cylinder comprised of three layers: tunica media, represented with muscle, tunica intima, represented with fibrotic plaque, and tunica adventitia, represented with collagen. These layers had mass densities 1.06 g/cm3, 1.22 g/cm3 and 1.07 g/cm3 respectively. The innermost layer consisted of calcified plaque of density 1.45 g/cm3 with varying thicknesses between 0.9 and 1.9 mm with an eccentric shape and a rough surface. The stents had similar composition to Boston Scientific Synergy stents and were modelled to not overlap. The Novoste Beta-Cath 3.5F IVBT device model was used, which has a 90Sr90Y source. The geometry is shown in Figure 1a. A cylindrical scoring geometry was implemented. Two set of simulations were performed. In the first simulation called water phantom, the entire system consisted of water with unit density, and dose to water was calculated similar to the clinical water based dosimetry. In the second simulation called the artery model proper material and mass densities were assigned to each component. To ensure uncertainties below 0.8% within a 1 mm radial distance to the source and 2% within 4.2 mm from the source, 100 million decay events were simulated. The Penelope physics list was used to simulate the electromagnetic interactions between particles. Average, minimum, and maximum dose was calculated at 2.0 mm from the source center and directly and 1 mm behind the outermost stents and guidewire. Absorbed dose was normalized to 23 Gy at 2.0 mm from the source center.
Results
International Conference on Monte Carlo Techniques for Medical Applications, 2022
Compared to the water phantom (Figure 1b), average dose in the artery model (Figure 1c) was attenuated by 50.9% at 2 mm from the source centre and directly behind the guidewire and outermost stent by 66.2%, and by 69.5% 1 mm behind this region. There was significant variation in dose around the source due to the guidewire attenuating dose the most, and heterogeneous distribution of calcification.
Discussion & Conclusions
Dosimetry for IVBT based on dose rate in water is not accurate. Heterogeneities need to be considered to deliver adequate dose to the lesion area. Stent material, heterogenous distribution of calcification and the off cantered placement of the guidewire affects the uniformity of dose distribution around the source. Patients may benefit from personalized treatment planning taking dose-attenuating by different tissue/material heterogeneities into account.
References
[1] Virani, Salim, S., et al. ""Heart Disease and Stroke Statistics—2020 Update"". Circulation, vol. 141, no. 9, March 03, 2020, pp. e336. doi: 10.1161/CIR.0000000000000757.
[2] Lee M, Banka G. In-stent restenosis. Interv Cardiol Clin 2016;5: 211e220.
[3] Chiu-Tsao ST, Schaart DR, Soares CG, et al. Dose calculation formalisms and consensus dosimetry parameters for intravascular brachytherapy dosimetry: Recommendations of the AAPM Therapy Physics Committee Task Group No. 149. Med Phys 2007;34: 4126e4157.
[4] Rivard MJ, Coursey BM, DeWerd LA, et al. Update of AAPM Task Group No. 43 Report: A revised AAPM protocol for brachytherapy dose calculations. Med Phys 2004;31:633e674.
[5] Nath R, Amols H, Coffey C, et al. Intravascular brachytherapy physics: Report of the AAPM Radiation Therapy Committee Task group No. 60. Med Phys 1999;26:119e152.’
[6] Agostinelli S, Allison J, Amako K, et al. Geant4da simulation toolkit. Nucl Instrum Methods Phys Res 2003;506:230e303."
Daoud, Youstina; Carroll, Liam; Enger, Shirin A.
A Radiation detector simulation toolkit for calculating the Arterial Input Function during Dynamic Positron Emission Tomography Inproceedings
In: International Conference on Monte Carlo Techniques for Medical Applications, 2022.
@inproceedings{nokey,
title = {A Radiation detector simulation toolkit for calculating the Arterial Input Function during Dynamic Positron Emission Tomography},
author = {Youstina Daoud and Liam Carroll and Shirin A. Enger},
year = {2022},
date = {2022-04-10},
booktitle = {International Conference on Monte Carlo Techniques for Medical Applications},
abstract = {"Introduction
Dynamic Positron Emission Tomography (dPET) is a functional imaging modality that provides an accurate assessment of patients’ physiological activities and response to treatments such as cancer, cardiac diseases and Alzheimer’s disease. It requires the measurement of the time-course activity concentration of the positron emitting PET radioisotopes in the patient’s arterial plasma, called the Arterial Input Function (AIF). The gold standard measurement of the AIF requires blood samples from the patient during the dPET. In our group, we are developing a non-invasive radiation detector that, placed on a patient’s wrist during the dPET scan, measures the number of positrons and photons escaping the radial artery and calculates the AIF. We have also developed a Modular Radiation Simulation Software for detector simulations called MaRSS that allows the user to run a Geant4-based Monte Carlo simulation, to calculate the AIF. Using the Monte Carlo method, MaRSS simulates a radioactive source decay in the radial artery and scores the amount of radiation escaping the radial artery and reaching the detector placed on the simulated patient’s wrist phantom. The wrist phantom is designed as a cylinder containing 2 holes that simulates the radial artery and vein. The shape and the depth of the radial artery vary between patients and proper knowledge of the distance between the radial artery and the skin, as well as its surface area, is important to accurately design the wrist phantom. Therefore, our aim was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, provide tools to measure the distance between the radial artery and the skin as well as the radial artery’s surface area and to create the necessary input file to MaRSS. The GUI provides MaRSS with a patient specific and more accurate wrist phantom, providing a patient-specific and more accurate calculation of the AIF without knowledge of C++ or Geant4.
Materials & Methods
The GUI elements were implemented using the multi-platform application and widget toolkit Qt 5 [1]. The C++ library, VTK 8.2.0 [2] was integrated in the GUI, which enables the user to import and manipulate the 2D ultrasound images. The toolkit comprises a measurement tool, a visualization window, a detector tab, a radiation source tab and MaRSS which is its simulation tool. To create an accurate wrist phantom, three 2D – cross secctional ultrasound scans of the patient’s wrist at 2 cm, 4 cm and 6 cm from the wrist crease and 1 longitudinal scan along the radial artery may be acquired and saved in DICOM format. In our case the BK3000 ultrasound system is used. These scans are imported into the GUI by selecting the folder that contains the images. Using the measurement functionalities shown in the top left corner of Figure 1, the surface of the radial artery is measured by drawing an ellipse on the artery’s boundary, then the toolkit measures the surface of the drawn ellipse and displays it in the Measurement window. The artery’s depth is also measured and displayed by drawing a straight line between the artery’s boundary and the skin. Using the left and right arrows, the user can navigate through the selected folder and measure the artery’s surface and depth on the other scans. The top right corner of the GUI shown in Figure 1, illustrates a Detector tab and a Source tab. The Detector tab allows the user to import a detector in STL format and place it on the ultrasound scan to simulate different setups of the detector, this functionality is still under development and
International Conference on Monte Carlo Techniques for Medical Applications, 2022
is optional. The Source tab allows the user to add the radioactive source used during the dPET by entering its mass number and its atomic number. After completing the 3 mandatory steps : import of the scan, measurement of different parameters extracted from the scan and choice of the radioactive source, the user can run the simulation by clicking on Run Simulation in the Simulation menu. The toolkit runs the MaRSS and creates the wrist phantom using the artery’s surface and depth measured by the user, then starts the decay of the chosen source placed randomly inside the artery.
Results
This toolkit allows the user to import 2D ultrasound scans and measure the radial artery’s surface and depth along the wrist, choose the radioactive source from the Source drop-down menu and specify the detector position. An input file to the MaRSS is thus created providing the required information to simulate the wrist phantom, the source and the detector’s position in MaRSS. The Run Simulation tab displays the output of the simulation in the GUI making it the only used tool for setting up the simulation and viewing the results.
Discussion & Conclusions
This toolkit enables the user to run a Geant4 Monte Carlo based simulation for detector development applications in 3 easy steps, not requiring any programming knowledge.
References
[1] Blanchette J, Summerfield M. C++ GUI programming with Qt 4: Prentice Hall Professional; 2006.
[2] Schroeder WJ, Avila LS, Hoffman W. Visualizing with VTK: a tutorial. IEEE Computer graphics and applications. 2000;20(5):20-7.
Acknowledgements This research was undertaken,in part, thanks to funding from the Canada Research Chairs Program (grant # 252135) as well as CHRP (NSERC+CIHR grant 170620).
"},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
"Introduction
Dynamic Positron Emission Tomography (dPET) is a functional imaging modality that provides an accurate assessment of patients’ physiological activities and response to treatments such as cancer, cardiac diseases and Alzheimer’s disease. It requires the measurement of the time-course activity concentration of the positron emitting PET radioisotopes in the patient’s arterial plasma, called the Arterial Input Function (AIF). The gold standard measurement of the AIF requires blood samples from the patient during the dPET. In our group, we are developing a non-invasive radiation detector that, placed on a patient’s wrist during the dPET scan, measures the number of positrons and photons escaping the radial artery and calculates the AIF. We have also developed a Modular Radiation Simulation Software for detector simulations called MaRSS that allows the user to run a Geant4-based Monte Carlo simulation, to calculate the AIF. Using the Monte Carlo method, MaRSS simulates a radioactive source decay in the radial artery and scores the amount of radiation escaping the radial artery and reaching the detector placed on the simulated patient’s wrist phantom. The wrist phantom is designed as a cylinder containing 2 holes that simulates the radial artery and vein. The shape and the depth of the radial artery vary between patients and proper knowledge of the distance between the radial artery and the skin, as well as its surface area, is important to accurately design the wrist phantom. Therefore, our aim was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, provide tools to measure the distance between the radial artery and the skin as well as the radial artery’s surface area and to create the necessary input file to MaRSS. The GUI provides MaRSS with a patient specific and more accurate wrist phantom, providing a patient-specific and more accurate calculation of the AIF without knowledge of C++ or Geant4.
Materials & Methods
The GUI elements were implemented using the multi-platform application and widget toolkit Qt 5 [1]. The C++ library, VTK 8.2.0 [2] was integrated in the GUI, which enables the user to import and manipulate the 2D ultrasound images. The toolkit comprises a measurement tool, a visualization window, a detector tab, a radiation source tab and MaRSS which is its simulation tool. To create an accurate wrist phantom, three 2D – cross secctional ultrasound scans of the patient’s wrist at 2 cm, 4 cm and 6 cm from the wrist crease and 1 longitudinal scan along the radial artery may be acquired and saved in DICOM format. In our case the BK3000 ultrasound system is used. These scans are imported into the GUI by selecting the folder that contains the images. Using the measurement functionalities shown in the top left corner of Figure 1, the surface of the radial artery is measured by drawing an ellipse on the artery’s boundary, then the toolkit measures the surface of the drawn ellipse and displays it in the Measurement window. The artery’s depth is also measured and displayed by drawing a straight line between the artery’s boundary and the skin. Using the left and right arrows, the user can navigate through the selected folder and measure the artery’s surface and depth on the other scans. The top right corner of the GUI shown in Figure 1, illustrates a Detector tab and a Source tab. The Detector tab allows the user to import a detector in STL format and place it on the ultrasound scan to simulate different setups of the detector, this functionality is still under development and
International Conference on Monte Carlo Techniques for Medical Applications, 2022
is optional. The Source tab allows the user to add the radioactive source used during the dPET by entering its mass number and its atomic number. After completing the 3 mandatory steps : import of the scan, measurement of different parameters extracted from the scan and choice of the radioactive source, the user can run the simulation by clicking on Run Simulation in the Simulation menu. The toolkit runs the MaRSS and creates the wrist phantom using the artery’s surface and depth measured by the user, then starts the decay of the chosen source placed randomly inside the artery.
Results
This toolkit allows the user to import 2D ultrasound scans and measure the radial artery’s surface and depth along the wrist, choose the radioactive source from the Source drop-down menu and specify the detector position. An input file to the MaRSS is thus created providing the required information to simulate the wrist phantom, the source and the detector’s position in MaRSS. The Run Simulation tab displays the output of the simulation in the GUI making it the only used tool for setting up the simulation and viewing the results.
Discussion & Conclusions
This toolkit enables the user to run a Geant4 Monte Carlo based simulation for detector development applications in 3 easy steps, not requiring any programming knowledge.
References
[1] Blanchette J, Summerfield M. C++ GUI programming with Qt 4: Prentice Hall Professional; 2006.
[2] Schroeder WJ, Avila LS, Hoffman W. Visualizing with VTK: a tutorial. IEEE Computer graphics and applications. 2000;20(5):20-7.
Acknowledgements This research was undertaken,in part, thanks to funding from the Canada Research Chairs Program (grant # 252135) as well as CHRP (NSERC+CIHR grant 170620).
"
Dynamic Positron Emission Tomography (dPET) is a functional imaging modality that provides an accurate assessment of patients’ physiological activities and response to treatments such as cancer, cardiac diseases and Alzheimer’s disease. It requires the measurement of the time-course activity concentration of the positron emitting PET radioisotopes in the patient’s arterial plasma, called the Arterial Input Function (AIF). The gold standard measurement of the AIF requires blood samples from the patient during the dPET. In our group, we are developing a non-invasive radiation detector that, placed on a patient’s wrist during the dPET scan, measures the number of positrons and photons escaping the radial artery and calculates the AIF. We have also developed a Modular Radiation Simulation Software for detector simulations called MaRSS that allows the user to run a Geant4-based Monte Carlo simulation, to calculate the AIF. Using the Monte Carlo method, MaRSS simulates a radioactive source decay in the radial artery and scores the amount of radiation escaping the radial artery and reaching the detector placed on the simulated patient’s wrist phantom. The wrist phantom is designed as a cylinder containing 2 holes that simulates the radial artery and vein. The shape and the depth of the radial artery vary between patients and proper knowledge of the distance between the radial artery and the skin, as well as its surface area, is important to accurately design the wrist phantom. Therefore, our aim was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, provide tools to measure the distance between the radial artery and the skin as well as the radial artery’s surface area and to create the necessary input file to MaRSS. The GUI provides MaRSS with a patient specific and more accurate wrist phantom, providing a patient-specific and more accurate calculation of the AIF without knowledge of C++ or Geant4.
Materials & Methods
The GUI elements were implemented using the multi-platform application and widget toolkit Qt 5 [1]. The C++ library, VTK 8.2.0 [2] was integrated in the GUI, which enables the user to import and manipulate the 2D ultrasound images. The toolkit comprises a measurement tool, a visualization window, a detector tab, a radiation source tab and MaRSS which is its simulation tool. To create an accurate wrist phantom, three 2D – cross secctional ultrasound scans of the patient’s wrist at 2 cm, 4 cm and 6 cm from the wrist crease and 1 longitudinal scan along the radial artery may be acquired and saved in DICOM format. In our case the BK3000 ultrasound system is used. These scans are imported into the GUI by selecting the folder that contains the images. Using the measurement functionalities shown in the top left corner of Figure 1, the surface of the radial artery is measured by drawing an ellipse on the artery’s boundary, then the toolkit measures the surface of the drawn ellipse and displays it in the Measurement window. The artery’s depth is also measured and displayed by drawing a straight line between the artery’s boundary and the skin. Using the left and right arrows, the user can navigate through the selected folder and measure the artery’s surface and depth on the other scans. The top right corner of the GUI shown in Figure 1, illustrates a Detector tab and a Source tab. The Detector tab allows the user to import a detector in STL format and place it on the ultrasound scan to simulate different setups of the detector, this functionality is still under development and
International Conference on Monte Carlo Techniques for Medical Applications, 2022
is optional. The Source tab allows the user to add the radioactive source used during the dPET by entering its mass number and its atomic number. After completing the 3 mandatory steps : import of the scan, measurement of different parameters extracted from the scan and choice of the radioactive source, the user can run the simulation by clicking on Run Simulation in the Simulation menu. The toolkit runs the MaRSS and creates the wrist phantom using the artery’s surface and depth measured by the user, then starts the decay of the chosen source placed randomly inside the artery.
Results
This toolkit allows the user to import 2D ultrasound scans and measure the radial artery’s surface and depth along the wrist, choose the radioactive source from the Source drop-down menu and specify the detector position. An input file to the MaRSS is thus created providing the required information to simulate the wrist phantom, the source and the detector’s position in MaRSS. The Run Simulation tab displays the output of the simulation in the GUI making it the only used tool for setting up the simulation and viewing the results.
Discussion & Conclusions
This toolkit enables the user to run a Geant4 Monte Carlo based simulation for detector development applications in 3 easy steps, not requiring any programming knowledge.
References
[1] Blanchette J, Summerfield M. C++ GUI programming with Qt 4: Prentice Hall Professional; 2006.
[2] Schroeder WJ, Avila LS, Hoffman W. Visualizing with VTK: a tutorial. IEEE Computer graphics and applications. 2000;20(5):20-7.
Acknowledgements This research was undertaken,in part, thanks to funding from the Canada Research Chairs Program (grant # 252135) as well as CHRP (NSERC+CIHR grant 170620).
"
Enger, Shirin A.; Sankey, Jack; Childress, Lilian; Megroureche, Julien
Radiation dosimeter Patent
2022, (US Patent App. 17/298,743).
@patent{enger2022radiation,
title = {Radiation dosimeter},
author = {Shirin A. Enger and Jack Sankey and Lilian Childress and Julien Megroureche},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
publisher = {Google Patents},
note = {US Patent App. 17/298,743},
keywords = {},
pubstate = {published},
tppubtype = {patent}
}
Jafarzadeh, Hossein; Mao, Ximeng; Enger, Shirin A.
Bayesian Optimization in Treatment Planning of High Dose Rate Brachytherapy Inproceedings
In: MEDICAL PHYSICS, pp. E200–E200, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{jafarzadeh2022bayesian,
title = {Bayesian Optimization in Treatment Planning of High Dose Rate Brachytherapy},
author = { Hossein Jafarzadeh and Ximeng Mao and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
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Thibodeau-Antonacci, Alana; Enger, Shirin A.; Bekerat, Hamed; Vuong, Te
Gafchromic film and scintillator detector measurements in phantom with a novel intensity-modulated brachytherapy endorectal shield Inproceedings
In: MEDICAL PHYSICS, pp. 5688–5689, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{thibodeau2022gafchromic,
title = {Gafchromic film and scintillator detector measurements in phantom with a novel intensity-modulated brachytherapy endorectal shield},
author = {Alana Thibodeau-Antonacci and Shirin A. Enger and Hamed Bekerat and Te Vuong},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
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Martinez, Victor Daniel Diaz; Cyr, Melodie; Slobodan, Devic; Tomic, Nada; Lewis, David F; Enger, Shirin A.
Use of the Monte Carlo Method to Relate GAFCHROMIC (R) EBT3 Film Response to Absorbed Dose for Alpha Particle Dosimetry Inproceedings
In: MEDICAL PHYSICS, pp. 5653–5653, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{martinez2022use,
title = {Use of the Monte Carlo Method to Relate GAFCHROMIC (R) EBT3 Film Response to Absorbed Dose for Alpha Particle Dosimetry},
author = {Victor Daniel Diaz Martinez and Melodie Cyr and Devic Slobodan and Nada Tomic and David F Lewis and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
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Bian, Jingyi; Duran, Juan; Shin, Wook-Geun; Ramos-Mendez, Jose; Childress, Lilian; Sankey, Jack; Seuntjens, Jan; Enger, Shirin A.
Update of the GEANT4-DNA Software for Simulation of Radiation Chemical Yield for Reactive Water Radiolysis Species at Different Temperature and PH Inproceedings
In: MEDICAL PHYSICS, pp. E911–E912, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{bian2022update,
title = {Update of the GEANT4-DNA Software for Simulation of Radiation Chemical Yield for Reactive Water Radiolysis Species at Different Temperature and PH},
author = {Jingyi Bian and Juan Duran and Wook-Geun Shin and Jose Ramos-Mendez and Lilian Childress and Jack Sankey and Jan Seuntjens and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
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organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
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Carroll, Liam; Enger, Shirin A.
Non-invasive measurement of the arterial input function for dynamic positron emission tomography: Simulation of clinical workflow Inproceedings
In: MEDICAL PHYSICS, pp. 5643–5644, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{carroll2022non,
title = {Non-invasive measurement of the arterial input function for dynamic positron emission tomography: Simulation of clinical workflow},
author = {Liam Carroll and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
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Dev, Sachin; Behmand, Behnaz; Pater, Piotr; Enger, Shirin A.
To Establish Correlation Between Physical Microdosimetric Quantities Such as Dose Mean Lineal Energy, Target Size and Biological Endpoints Such as DNA Double Strand Breaks in HeLa Cells Irradiated with Iridium-192 High Dose Rate Brachytherapy Source and 225kV X-Rays Inproceedings
In: MEDICAL PHYSICS, pp. E345–E345, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{dev2022establish,
title = {To Establish Correlation Between Physical Microdosimetric Quantities Such as Dose Mean Lineal Energy, Target Size and Biological Endpoints Such as DNA Double Strand Breaks in HeLa Cells Irradiated with Iridium-192 High Dose Rate Brachytherapy Source and 225kV X-Rays},
author = {Sachin Dev and Behnaz Behmand and Piotr Pater and Shirin A. Enger},
year = {2022},
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}
Babik, Joud; Chabaytah, Naim; Behmand, Behnaz; Connell, Tanner; Evans, Michael; Ruo, Russell; Poirier, Y; Enger, Shirin A.
Characterization of the Relative Biological Effectiveness of a Range of Photon Energies for Irradiation of HeLa and PC-3 Cell Lines Inproceedings
In: MEDICAL PHYSICS, pp. E980–E980, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{babik2022characterization,
title = {Characterization of the Relative Biological Effectiveness of a Range of Photon Energies for Irradiation of HeLa and PC-3 Cell Lines},
author = {Joud Babik and Naim Chabaytah and Behnaz Behmand and Tanner Connell and Michael Evans and Russell Ruo and Y Poirier and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
pages = {E980--E980},
organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
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pubstate = {published},
tppubtype = {inproceedings}
}
Martinez, Victor Daniel Diaz; Carroll, Liam; Enger, Shirin A.
Monte Carlo Simulation of the 224Ra Decay Chain and the Diffusion of 220Rn for Diffusing Alpha-Emitters Radiotherapy Inproceedings
In: MEDICAL PHYSICS, pp. E828–E828, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{martinez2022monte,
title = {Monte Carlo Simulation of the 224Ra Decay Chain and the Diffusion of 220Rn for Diffusing Alpha-Emitters Radiotherapy},
author = {Victor Daniel Diaz Martinez and Liam Carroll and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
pages = {E828--E828},
organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Berumen-Murillo, Francisco; Enger, Shirin A.; Beaulieu, Luc
Sub-Second D (M, M) Calculation for LDR Prostate Brachytherapy Using Deep Learning Methods Inproceedings
In: MEDICAL PHYSICS, pp. E163–E163, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{berumen2022sub,
title = {Sub-Second D (M, M) Calculation for LDR Prostate Brachytherapy Using Deep Learning Methods},
author = {Francisco Berumen-Murillo and Shirin A. Enger and Luc Beaulieu},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
pages = {E163--E163},
organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
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}
Han, Dae Yup; Damato, Antonio; Enger, Shirin A.; Roberts, K; Prisciandaro, J; Han, D; Nunez, D
Emerging and Re-Emerging Brachytherapy Treatments Inproceedings
In: MEDICAL PHYSICS, pp. E356–E356, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{han2022emerging,
title = {Emerging and Re-Emerging Brachytherapy Treatments},
author = {Dae Yup Han and Antonio Damato and Shirin A. Enger and K Roberts and J Prisciandaro and D Han and D Nunez},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
pages = {E356--E356},
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}
Zou, Yujing; Lecavalier-barsoum, Magali; Pelmus, Manuela; Enger, Shirin A.
In: MEDICAL PHYSICS, pp. E266–E266, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{zou2022patient,
title = {Patient-Specific Nuclei Size and Cell Spacing Distribution Extraction From Histopathology Whole Slide Images for Treatment Outcome Prediction Modelling},
author = {Yujing Zou and Magali Lecavalier-barsoum and Manuela Pelmus and Shirin A. Enger},
url = {https://w4.aapm.org/meetings/2022AM/programInfo/programAbs.php?sid=10686&aid=66642},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
pages = {E266--E266},
organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
abstract = {MO-C930-IePD-F5-1 (Monday, 7/11/2022) 9:30 AM - 10:00 AM [Eastern Time (GMT-4)]
Exhibit Hall | Forum 5
Purpose: To deliver a fully automated and generalizable approach extracting patient-specific nuclei size (ns) and cell spacing (cs) distributions from cancerous and non-tumoral regions of hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) for gynecological cancer multiscale treatment outcome modelling.
Methods: Each pre-treatment gigapixel H&E WSI digitized at 40 x magnification (0.2482 microns/pixel) were divided into 5000 x 5000-pixel patches. Within each patch, the nucleus centers were identified by a difference of gaussians blob detection algorithm obtaining Delaunay triangulations and Voronoi diagrams providing cs radius. The ns radius was computed from stained pixels dominated by hematoxylin content with an automatic thresholding algorithm. With multiprocessing CPUs on a PC for each WSI, eight feature types were calculated preserving biopsies tissue heterogeneity: the mean and standard deviation of cs and ns distributions concatenated from all patches for cancerous and non-tumoral regions. This method was applied to 40 patients (1 WSI per patient) with treatment outcomes of post radiation-therapy (RT) recurrence (n = 9),and death (n = 8)).
Results: The WSI cancerous region cs distribution mean among patients without post-RT recurrence has a median of 6.64 microns, and those with post-RT recurrence with a median of 7.11 microns. This indicates the potential of utilizing such distribution features in treatment outcome prognosis modelling. Furthermore, at the third quartile, the WSI non-tumoral region ns distribution standard deviation among patients without post-RT recurrence has a value of 2.16 microns, and 1.46 microns for those with post-RT recurrence.
Conclusion: Our approach derives patient-specific microscopic data distributions from histopathology WSI that can be directly associated with retrospective patient outcomes. They are complementary and spatially orthogonal to information served by other medical imaging modalities such as CT, MR, and Ultrasound. Therefore, it has the unique potential to augment treatment outcome model inference when properly fused with radiological scans.
Funding Support, Disclosures, and Conflict of Interest: CIHR grant number 103548 and Canada Research Chairs Program (grant #252135)
Keywords
Image Analysis, Feature Extraction, Radiation Therapy
Taxonomy
IM/TH- Image Analysis (Single Modality or Multi-Modality): Imaging biomarkers and radiomics
Contact Email
yujing.zou@mail.mcgill.ca},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
MO-C930-IePD-F5-1 (Monday, 7/11/2022) 9:30 AM - 10:00 AM [Eastern Time (GMT-4)]
Exhibit Hall | Forum 5
Purpose: To deliver a fully automated and generalizable approach extracting patient-specific nuclei size (ns) and cell spacing (cs) distributions from cancerous and non-tumoral regions of hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) for gynecological cancer multiscale treatment outcome modelling.
Methods: Each pre-treatment gigapixel H&E WSI digitized at 40 x magnification (0.2482 microns/pixel) were divided into 5000 x 5000-pixel patches. Within each patch, the nucleus centers were identified by a difference of gaussians blob detection algorithm obtaining Delaunay triangulations and Voronoi diagrams providing cs radius. The ns radius was computed from stained pixels dominated by hematoxylin content with an automatic thresholding algorithm. With multiprocessing CPUs on a PC for each WSI, eight feature types were calculated preserving biopsies tissue heterogeneity: the mean and standard deviation of cs and ns distributions concatenated from all patches for cancerous and non-tumoral regions. This method was applied to 40 patients (1 WSI per patient) with treatment outcomes of post radiation-therapy (RT) recurrence (n = 9),and death (n = 8)).
Results: The WSI cancerous region cs distribution mean among patients without post-RT recurrence has a median of 6.64 microns, and those with post-RT recurrence with a median of 7.11 microns. This indicates the potential of utilizing such distribution features in treatment outcome prognosis modelling. Furthermore, at the third quartile, the WSI non-tumoral region ns distribution standard deviation among patients without post-RT recurrence has a value of 2.16 microns, and 1.46 microns for those with post-RT recurrence.
Conclusion: Our approach derives patient-specific microscopic data distributions from histopathology WSI that can be directly associated with retrospective patient outcomes. They are complementary and spatially orthogonal to information served by other medical imaging modalities such as CT, MR, and Ultrasound. Therefore, it has the unique potential to augment treatment outcome model inference when properly fused with radiological scans.
Funding Support, Disclosures, and Conflict of Interest: CIHR grant number 103548 and Canada Research Chairs Program (grant #252135)
Keywords
Image Analysis, Feature Extraction, Radiation Therapy
Taxonomy
IM/TH- Image Analysis (Single Modality or Multi-Modality): Imaging biomarkers and radiomics
Contact Email
yujing.zou@mail.mcgill.ca
Exhibit Hall | Forum 5
Purpose: To deliver a fully automated and generalizable approach extracting patient-specific nuclei size (ns) and cell spacing (cs) distributions from cancerous and non-tumoral regions of hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) for gynecological cancer multiscale treatment outcome modelling.
Methods: Each pre-treatment gigapixel H&E WSI digitized at 40 x magnification (0.2482 microns/pixel) were divided into 5000 x 5000-pixel patches. Within each patch, the nucleus centers were identified by a difference of gaussians blob detection algorithm obtaining Delaunay triangulations and Voronoi diagrams providing cs radius. The ns radius was computed from stained pixels dominated by hematoxylin content with an automatic thresholding algorithm. With multiprocessing CPUs on a PC for each WSI, eight feature types were calculated preserving biopsies tissue heterogeneity: the mean and standard deviation of cs and ns distributions concatenated from all patches for cancerous and non-tumoral regions. This method was applied to 40 patients (1 WSI per patient) with treatment outcomes of post radiation-therapy (RT) recurrence (n = 9),and death (n = 8)).
Results: The WSI cancerous region cs distribution mean among patients without post-RT recurrence has a median of 6.64 microns, and those with post-RT recurrence with a median of 7.11 microns. This indicates the potential of utilizing such distribution features in treatment outcome prognosis modelling. Furthermore, at the third quartile, the WSI non-tumoral region ns distribution standard deviation among patients without post-RT recurrence has a value of 2.16 microns, and 1.46 microns for those with post-RT recurrence.
Conclusion: Our approach derives patient-specific microscopic data distributions from histopathology WSI that can be directly associated with retrospective patient outcomes. They are complementary and spatially orthogonal to information served by other medical imaging modalities such as CT, MR, and Ultrasound. Therefore, it has the unique potential to augment treatment outcome model inference when properly fused with radiological scans.
Funding Support, Disclosures, and Conflict of Interest: CIHR grant number 103548 and Canada Research Chairs Program (grant #252135)
Keywords
Image Analysis, Feature Extraction, Radiation Therapy
Taxonomy
IM/TH- Image Analysis (Single Modality or Multi-Modality): Imaging biomarkers and radiomics
Contact Email
yujing.zou@mail.mcgill.ca
Thibodeau-Antonacci, Alana; Vuong, Te; Liontis, B; Rayes, F; Pande, S; Enger, Shirin A.
Development of a Novel MRI-Compatible Applicator for Intensity Modulated Rectal Brachytherapy Inproceedings
In: MEDICAL PHYSICS, pp. E240–E240, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{thibodeau2022development,
title = {Development of a Novel MRI-Compatible Applicator for Intensity Modulated Rectal Brachytherapy},
author = {Alana Thibodeau-Antonacci and Te Vuong and B Liontis and F Rayes and S Pande and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
pages = {E240--E240},
organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
keywords = {},
pubstate = {published},
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}
Weishaupt, Luca L.; Vuong, Te; Thibodeau-Antonacci, Alana; Garant, A; Singh, K; Miller, C; Martin, A; Schmitt-Ulms, F; Enger, Shirin A.
PO-1325 Automated rectal tumor segmentation with inter-observer variability-based uncertainty estimates Journal Article
In: Radiotherapy and Oncology, vol. 170, pp. S1120–S1121, 2022.
@article{weishaupt2022po,
title = {PO-1325 Automated rectal tumor segmentation with inter-observer variability-based uncertainty estimates},
author = {Luca L. Weishaupt and Te Vuong and Alana Thibodeau-Antonacci and A Garant and K Singh and C Miller and A Martin and F Schmitt-Ulms and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Radiotherapy and Oncology},
volume = {170},
pages = {S1120--S1121},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zou, Yujing; Weishaupt, Luca; Enger, Shirin A.
SP-0014 McMedHacks: Deep learning for medical image analysis workshops and Hackathon in radiation oncology Journal Article
In: Radiotherapy and Oncology, vol. 170, pp. S4–S5, 2022.
@article{zou2022sp,
title = {SP-0014 McMedHacks: Deep learning for medical image analysis workshops and Hackathon in radiation oncology},
author = {Yujing Zou and Luca Weishaupt and Shirin A. Enger},
url = {https://www-sciencedirect-com.proxy3.library.mcgill.ca/science/article/pii/S0167814022038695?via%3Dihub},
doi = {10.1016/S0167-8140(22)03869-5},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Radiotherapy and Oncology},
volume = {170},
pages = {S4--S5},
publisher = {Elsevier},
abstract = {Purpose/Objective: The McMedHacks workshop and presentation series was created to teach individuals from various backgrounds about deep learning (DL) for medical image analysis in May, 2021. Material/Methods: McMedHacks is a free and student-led 8-week summer program. Registration for the event was open to everyone, including a form to survey participants’ area of expertise, country of origin, level of study, and level of programming skills. The weekly workshops were instructed by 8 students and experts assisted by 20 mentors who provided weekly tutorials. Recent developments in DL and medical physics were highlighted by 21 leaders from industry and academia. A virtual grand challenge Hackathon took place at the end of the workshop series. All events were held virtually and recorded on Zoom to accommodate all time zones and locations. The workshops were designed as interactive coding demos and shared through Google Colab notebooks. Results: McMedHacks gained 356 registrations from participants of 38 different countries (Fig. 1) from undergraduates, to PhDs and MDs. A vast number of disciplines and professions were represented, dominated by medical physics students, academic, and clinical medical physicists (Fig. 2). Sixty-nine participants earned a certificate of completion by having engaged with at least 12 of all 14 events. The program received participant feedback average scores of 4.768, 4.478, 4.579, 4.292, 4.84 out of five for the qualities of presentation, workshop session, tutorial and mentor, assignments, and course delivery, respectively. The eight-week long workshop’s duration allowed participants to digest the taught materials in a continuous manner as opposed to bootcamp-style conference workshops. Conclusion: The overwhelming interest and engagement for the McMedHacks workshop series from the Radiation Oncology (RadOnc) community illustrates a demand for Artificial Intelligence (AI) education in RadOnc. The future of RadOnc clinics will inevitably integrate AI. Therefore, current RadOnc professionals, and student and resident trainees should be prepared to understand basic AI principles and its applications to troubleshoot, innovate, and collaborate. McMedHacks set an excellent example of promoting open and multidisciplinary education, scientific communication, and leadership for integrating AI education into the RadOnc community on an international level. Therefore, we advocate for implementation of AI curriculums in professional education programs such as Commission on Accreditation of Medical Physics Education Programs (CAMPEP). Furthermore, we encourage experts from around the world in the field of AI, or RadOnc, or both, to take initiatives like McMedHacks to collaborate and push forward AI education in their departments and lead practical workshops, regardless of their levels of education.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose/Objective: The McMedHacks workshop and presentation series was created to teach individuals from various backgrounds about deep learning (DL) for medical image analysis in May, 2021. Material/Methods: McMedHacks is a free and student-led 8-week summer program. Registration for the event was open to everyone, including a form to survey participants’ area of expertise, country of origin, level of study, and level of programming skills. The weekly workshops were instructed by 8 students and experts assisted by 20 mentors who provided weekly tutorials. Recent developments in DL and medical physics were highlighted by 21 leaders from industry and academia. A virtual grand challenge Hackathon took place at the end of the workshop series. All events were held virtually and recorded on Zoom to accommodate all time zones and locations. The workshops were designed as interactive coding demos and shared through Google Colab notebooks. Results: McMedHacks gained 356 registrations from participants of 38 different countries (Fig. 1) from undergraduates, to PhDs and MDs. A vast number of disciplines and professions were represented, dominated by medical physics students, academic, and clinical medical physicists (Fig. 2). Sixty-nine participants earned a certificate of completion by having engaged with at least 12 of all 14 events. The program received participant feedback average scores of 4.768, 4.478, 4.579, 4.292, 4.84 out of five for the qualities of presentation, workshop session, tutorial and mentor, assignments, and course delivery, respectively. The eight-week long workshop’s duration allowed participants to digest the taught materials in a continuous manner as opposed to bootcamp-style conference workshops. Conclusion: The overwhelming interest and engagement for the McMedHacks workshop series from the Radiation Oncology (RadOnc) community illustrates a demand for Artificial Intelligence (AI) education in RadOnc. The future of RadOnc clinics will inevitably integrate AI. Therefore, current RadOnc professionals, and student and resident trainees should be prepared to understand basic AI principles and its applications to troubleshoot, innovate, and collaborate. McMedHacks set an excellent example of promoting open and multidisciplinary education, scientific communication, and leadership for integrating AI education into the RadOnc community on an international level. Therefore, we advocate for implementation of AI curriculums in professional education programs such as Commission on Accreditation of Medical Physics Education Programs (CAMPEP). Furthermore, we encourage experts from around the world in the field of AI, or RadOnc, or both, to take initiatives like McMedHacks to collaborate and push forward AI education in their departments and lead practical workshops, regardless of their levels of education.
Enger, Shirin A.
SP-0706 Creating a'successful'work environment Journal Article
In: Radiotherapy and Oncology, vol. 170, pp. S620, 2022.
@article{enger2022sp,
title = {SP-0706 Creating a'successful'work environment},
author = {Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Radiotherapy and Oncology},
volume = {170},
pages = {S620},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vuong, Te; Garant, A; Khosrow-Khavar, F; Devic, S; Enger, Shirin A.; Boutros, M; Cohen, A; Miller, CS; Friedman, G; Galiatsatos, P; others,
A141 IS SURGERY STILL THE ONLY TREATMENT OPTION FOR CURABLE RECTAL CANCER? Journal Article
In: Journal of the Canadian Association of Gastroenterology, vol. 5, no. Suppl 1, pp. 13, 2022.
@article{vuong2022a141,
title = {A141 IS SURGERY STILL THE ONLY TREATMENT OPTION FOR CURABLE RECTAL CANCER?},
author = {Te Vuong and A Garant and F Khosrow-Khavar and S Devic and Shirin A. Enger and M Boutros and A Cohen and CS Miller and G Friedman and P Galiatsatos and others },
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of the Canadian Association of Gastroenterology},
volume = {5},
number = {Suppl 1},
pages = {13},
publisher = {Oxford University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Weishaupt, Luca L; Vuong, Te; Thibodeau-Antonacci, Alana; Garant, A; Singh, KS; Miller, C; Martin, A; Enger, Shirin A.
A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING Journal Article
In: Journal of the Canadian Association of Gastroenterology, vol. 5, no. Supplement_1, pp. 140–142, 2022.
@article{weishaupt2022a121,
title = {A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING},
author = {Luca L Weishaupt and Te Vuong and Alana Thibodeau-Antonacci and A Garant and KS Singh and C Miller and A Martin and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of the Canadian Association of Gastroenterology},
volume = {5},
number = {Supplement_1},
pages = {140--142},
publisher = {Oxford University Press US},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rodrigues-Machado, Fernanda C; Pestre, Pauline; Dumont, Vincent; Bernard, Simon; Janitz, Erika; Scanlon, Liam; Enger, Shirin A.; Childress, Lilian; Sankey, Jack
Sideband cavity absorption readout (SideCAR) with a robust frequency lock Journal Article
In: Optics Express, vol. 30, no. 2, pp. 754–767, 2022.
@article{rodrigues2022sideband,
title = {Sideband cavity absorption readout (SideCAR) with a robust frequency lock},
author = {Fernanda C Rodrigues-Machado and Pauline Pestre and Vincent Dumont and Simon Bernard and Erika Janitz and Liam Scanlon and Shirin A. Enger and Lilian Childress and Jack Sankey},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Optics Express},
volume = {30},
number = {2},
pages = {754--767},
publisher = {Optical Society of America},
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pubstate = {published},
tppubtype = {article}
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Carroll, Liam; Enger, Shirin A.
Monte Carlo Simulations of a Non-Invasive Positron Detector to Measure the Arterial Input Function for Dynamic PET Inproceedings
In: Journal of Physics: Conference Series, pp. 012005, IOP Publishing 2022.
@inproceedings{carroll2022monte,
title = {Monte Carlo Simulations of a Non-Invasive Positron Detector to Measure the Arterial Input Function for Dynamic PET},
author = {Liam Carroll and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Journal of Physics: Conference Series},
volume = {2167},
number = {1},
pages = {012005},
organization = {IOP Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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2021
Weishaupt, Luca L.; Thibodeau-Antonacci, Alana; Garant, Aurelie; Singh, Kelita; Miller, Corey; Vuong, Té; Enger, Shirin A.
Deep learning based tumor segmentation of endoscopy images for rectal cancer patients Presentation
ESTRO Annual meeting, 27.08.2021.
@misc{Weishaupt2021b,
title = {Deep learning based tumor segmentation of endoscopy images for rectal cancer patients},
author = {Luca L. Weishaupt and Alana Thibodeau-Antonacci and Aurelie Garant and Kelita Singh and Corey Miller and Té Vuong and Shirin A. Enger},
url = {https://www.estro.org/Congresses/ESTRO-2021/610/posterdiscussion34-deep-learningforauto-contouring/3710/deeplearning-basedtumorsegmentationofendoscopyimag},
year = {2021},
date = {2021-08-27},
urldate = {2021-08-27},
abstract = {Purpose or Objective
The objective of this study was to develop an automated rectal tumor segmentation algorithm from endoscopy images. The algorithm will be used in a future multimodal treatment outcome prediction model. Currently, treatment outcome prediction models rely on manual segmentations of regions of interest, which are prone to inter-observer variability. To quantify this human error and demonstrate the feasibility of automated endoscopy image segmentation, we compare three deep learning architectures.
Material and Methods
A gastrointestinal physician (G1) segmented 550 endoscopy images of rectal tumors into tumor and non-tumor regions. To quantify the inter-observer variability, a second gastrointestinal physician (G2) contoured 319 of the images independently.
The 550 images and annotations from G1 were divided into 408 training, 82 validation, and 60 testing sets. Three deep learning architectures were trained; a fully convolutional neural network (FCN32), a U-Net, and a SegNet. These architectures have been used for robust medical image segmentation in previous studies.
All models were trained on a CPU supercomputing cluster. Data augmentation in the form of random image transformations, including scaling, rotation, shearing, Gaussian blurring, and noise addition, was used to improve the models' robustness.
The neural networks' output went through a final layer of noise removal and hole filling before evaluation. Finally, the segmentations from G2 and the neural networks' predictions were compared against the ground truth labels from G1.
Results
The FCN32, U-Net, and SegNet had average segmentation times of 0.77, 0.48, and 0.43 seconds per image, respectively. The average segmentation time per image for G1 and G2 were 10 and 8 seconds, respectively.
All the ground truth labels contained tumors, but G2 and the deep learning models did not always find tumors in the images. The scores are based on the agreement of tumor contours with G1’s ground truth and were thus only computed for images in which tumor was found. The automated segmentation algorithms consistently achieved equal or better scores than G2's manual segmentations. G2's low F1/DICE and precision scores indicate poor agreement between the manual contours.
Conclusion
There is a need for robust and accurate segmentation algorithms for rectal tumor segmentation since manual segmentation of these tumors is susceptible to significant inter-observer variability. The deep learning-based segmentation algorithms proposed in this study are more efficient and achieved a higher agreement with our manual ground truth segmentations than a second expert annotator. Future studies will investigate how to train deep learning models on multiple ground truth annotations to prevent learning observer biases.},
howpublished = {ESTRO Annual meeting},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Purpose or Objective
The objective of this study was to develop an automated rectal tumor segmentation algorithm from endoscopy images. The algorithm will be used in a future multimodal treatment outcome prediction model. Currently, treatment outcome prediction models rely on manual segmentations of regions of interest, which are prone to inter-observer variability. To quantify this human error and demonstrate the feasibility of automated endoscopy image segmentation, we compare three deep learning architectures.
Material and Methods
A gastrointestinal physician (G1) segmented 550 endoscopy images of rectal tumors into tumor and non-tumor regions. To quantify the inter-observer variability, a second gastrointestinal physician (G2) contoured 319 of the images independently.
The 550 images and annotations from G1 were divided into 408 training, 82 validation, and 60 testing sets. Three deep learning architectures were trained; a fully convolutional neural network (FCN32), a U-Net, and a SegNet. These architectures have been used for robust medical image segmentation in previous studies.
All models were trained on a CPU supercomputing cluster. Data augmentation in the form of random image transformations, including scaling, rotation, shearing, Gaussian blurring, and noise addition, was used to improve the models' robustness.
The neural networks' output went through a final layer of noise removal and hole filling before evaluation. Finally, the segmentations from G2 and the neural networks' predictions were compared against the ground truth labels from G1.
Results
The FCN32, U-Net, and SegNet had average segmentation times of 0.77, 0.48, and 0.43 seconds per image, respectively. The average segmentation time per image for G1 and G2 were 10 and 8 seconds, respectively.
All the ground truth labels contained tumors, but G2 and the deep learning models did not always find tumors in the images. The scores are based on the agreement of tumor contours with G1’s ground truth and were thus only computed for images in which tumor was found. The automated segmentation algorithms consistently achieved equal or better scores than G2's manual segmentations. G2's low F1/DICE and precision scores indicate poor agreement between the manual contours.
Conclusion
There is a need for robust and accurate segmentation algorithms for rectal tumor segmentation since manual segmentation of these tumors is susceptible to significant inter-observer variability. The deep learning-based segmentation algorithms proposed in this study are more efficient and achieved a higher agreement with our manual ground truth segmentations than a second expert annotator. Future studies will investigate how to train deep learning models on multiple ground truth annotations to prevent learning observer biases.
The objective of this study was to develop an automated rectal tumor segmentation algorithm from endoscopy images. The algorithm will be used in a future multimodal treatment outcome prediction model. Currently, treatment outcome prediction models rely on manual segmentations of regions of interest, which are prone to inter-observer variability. To quantify this human error and demonstrate the feasibility of automated endoscopy image segmentation, we compare three deep learning architectures.
Material and Methods
A gastrointestinal physician (G1) segmented 550 endoscopy images of rectal tumors into tumor and non-tumor regions. To quantify the inter-observer variability, a second gastrointestinal physician (G2) contoured 319 of the images independently.
The 550 images and annotations from G1 were divided into 408 training, 82 validation, and 60 testing sets. Three deep learning architectures were trained; a fully convolutional neural network (FCN32), a U-Net, and a SegNet. These architectures have been used for robust medical image segmentation in previous studies.
All models were trained on a CPU supercomputing cluster. Data augmentation in the form of random image transformations, including scaling, rotation, shearing, Gaussian blurring, and noise addition, was used to improve the models' robustness.
The neural networks' output went through a final layer of noise removal and hole filling before evaluation. Finally, the segmentations from G2 and the neural networks' predictions were compared against the ground truth labels from G1.
Results
The FCN32, U-Net, and SegNet had average segmentation times of 0.77, 0.48, and 0.43 seconds per image, respectively. The average segmentation time per image for G1 and G2 were 10 and 8 seconds, respectively.
All the ground truth labels contained tumors, but G2 and the deep learning models did not always find tumors in the images. The scores are based on the agreement of tumor contours with G1’s ground truth and were thus only computed for images in which tumor was found. The automated segmentation algorithms consistently achieved equal or better scores than G2's manual segmentations. G2's low F1/DICE and precision scores indicate poor agreement between the manual contours.
Conclusion
There is a need for robust and accurate segmentation algorithms for rectal tumor segmentation since manual segmentation of these tumors is susceptible to significant inter-observer variability. The deep learning-based segmentation algorithms proposed in this study are more efficient and achieved a higher agreement with our manual ground truth segmentations than a second expert annotator. Future studies will investigate how to train deep learning models on multiple ground truth annotations to prevent learning observer biases.
DeCunha, Joseph M.; Villegas, Fernanda; Vallières, Martin; Torres, Jose; Camilleri-Broët, Sophie; Enger, Shirin A.
Patient-specific microdosimetry: a proof of concept Journal Article
In: Physics in Medicine and Biology, 2021, ISSN: 1361-6560.
@article{decunha_patient-specific_2021,
title = {Patient-specific microdosimetry: a proof of concept},
author = {Joseph M. DeCunha and Fernanda Villegas and Martin Vallières and Jose Torres and Sophie Camilleri-Broët and Shirin A. Enger},
doi = {10.1088/1361-6560/ac1d1e},
issn = {1361-6560},
year = {2021},
date = {2021-08-01},
journal = {Physics in Medicine and Biology},
abstract = {Microscopic energy deposition distributions from ionizing radiation are used to predict the biological effects of an irradiation and vary depending on biological target size. Ionizing radiation is thought to kill cells or inhibit cell cycling mainly by damaging DNA in the cell nucleus. The size of cells and nuclei depends on tissue type, cell cycle, and malignancy, all of which vary between patients. The aim of this study was to develop methods to perform patient-specific microdosimetry, that being, determining microdosimetric quantities in volumes that correspond to the sizes of cells and nuclei observed in a patient's tissue. A histopathological sample extracted from a stage I lung adenocarcinoma patient was analyzed. A pouring simulation was used to generate a three-dimensional tissue model from cell and nucleus size information determined from the histopathological sample. Microdosimetric distributions including f(y) and d(y) were determined for Co-60,Ir-192,Yb-169 and I-125 in a patient-specific model containing a distribution of cell and nucleus sizes. Fixed radius models and a summation method (where f(y) from many fixed radii models are summed) were compared to the full patient-specific model to evaluate their suitability for fast determination of patient-specific microdosimetric parameters. Fixed radius models do not provide a close approximation of the full patient-specific model y ̅_f or y ̅_d for the lower energy sources investigated, Yb-169 and I-125. The higher energy sources investigated, Co-60 and Ir-192 are less sensitive to target size variation than Yb-169 and I-125. A summation method yields the most accurate approximation of the full model d(y) for all radioisotopes investigated. A summation method allows for the computation of patient-specific microdosimetric distributions with the computing power of a personal computer. With appropriate biological inputs the microdosimetric distributions computed using these methods can yield a patient-specific relative biological effectiveness as part of a multiscale treatment planning approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Microscopic energy deposition distributions from ionizing radiation are used to predict the biological effects of an irradiation and vary depending on biological target size. Ionizing radiation is thought to kill cells or inhibit cell cycling mainly by damaging DNA in the cell nucleus. The size of cells and nuclei depends on tissue type, cell cycle, and malignancy, all of which vary between patients. The aim of this study was to develop methods to perform patient-specific microdosimetry, that being, determining microdosimetric quantities in volumes that correspond to the sizes of cells and nuclei observed in a patient's tissue. A histopathological sample extracted from a stage I lung adenocarcinoma patient was analyzed. A pouring simulation was used to generate a three-dimensional tissue model from cell and nucleus size information determined from the histopathological sample. Microdosimetric distributions including f(y) and d(y) were determined for Co-60,Ir-192,Yb-169 and I-125 in a patient-specific model containing a distribution of cell and nucleus sizes. Fixed radius models and a summation method (where f(y) from many fixed radii models are summed) were compared to the full patient-specific model to evaluate their suitability for fast determination of patient-specific microdosimetric parameters. Fixed radius models do not provide a close approximation of the full patient-specific model y ̅_f or y ̅_d for the lower energy sources investigated, Yb-169 and I-125. The higher energy sources investigated, Co-60 and Ir-192 are less sensitive to target size variation than Yb-169 and I-125. A summation method yields the most accurate approximation of the full model d(y) for all radioisotopes investigated. A summation method allows for the computation of patient-specific microdosimetric distributions with the computing power of a personal computer. With appropriate biological inputs the microdosimetric distributions computed using these methods can yield a patient-specific relative biological effectiveness as part of a multiscale treatment planning approach.
Lecavalier-Barsoum, Magali; Khosrow-Khavar, Farzin; Asiev, Krum; Popovic, Marija; Vuong, Te; Enger, Shirin A.
Utilization of brachytherapy in Quebec, Canada Journal Article
In: Brachytherapy, pp. S1538–4721(21)00452–9, 2021, ISSN: 1873-1449.
@article{lecavalier-barsoum_utilization_2021,
title = {Utilization of brachytherapy in Quebec, Canada},
author = {Magali Lecavalier-Barsoum and Farzin Khosrow-Khavar and Krum Asiev and Marija Popovic and Te Vuong and Shirin A. Enger},
doi = {10.1016/j.brachy.2021.07.002},
issn = {1873-1449},
year = {2021},
date = {2021-08-01},
journal = {Brachytherapy},
pages = {S1538--4721(21)00452--9},
abstract = {BACKGROUND AND PURPOSE: Despite the excellent clinical outcomes from brachytherapy treatments compared with other modalities and the low associated costs, there have been reports of a decline in utilization of brachytherapy. The aim of this study was to investigate in detail the trend in utilization of brachytherapy in the province of Québec, Canada, from 2011 to 2019.
MATERIALS AND METHODS: All radiotherapy clinics in the province of Quebec, and among these the clinics that provide brachytherapy treatments, were identified. This observational retrospective cohort study involved analysis of data compiled by the Ministère de la Santé et des Services Sociaux du Québec for the period of 2011 to end of 2019 on all brachytherapy procedures performed in the province of Quebec. Time series graphs were used to describe the number of high dose rate (HDR) and low dose rate (LDR) brachytherapy treatments during the studied time period. Statistical analysis was conducted using R statistical software.
RESULTS: Between 2011 and 2019, 12 hospitals in the province of Québec provided radiotherapy treatments, and all of them offered brachytherapy services. The median annual number of brachytherapy sessions was 4413 (range 3930-4829). HDR brachytherapy represented over 90% of all brachytherapy treatments throughout the study period. Significant changes over time were observed in the number of treatments: at least 5% change was seen only for the two most common subtypes of brachytherapy, HDR interstitial and HDR intracavitary, with an increase of 9.6% and a decrease of 9.2%, respectively. The use of other subtypes of brachytherapy (HDR-plesiotherapy, LDR-interstitial, LDR-intracavitary, LDR-eye plaque) was stable between 2011 and 2019, with ≤ 2.5% variation.
CONCLUSION: This study demonstrates an overall steady use of brachytherapy between 2011 and 2019 in Quebec. Brachytherapy offers numerous advantages for the treatment of diverse cancer sites. Although more sophisticated external beam radiotherapy treatments have emerged in the last decades, the precision and cost-effectiveness of brachytherapy remain unbeaten. To ensure the continued use and availability of brachytherapy, governments must put in place policies and regulations to that effect. Training and exposure of future health care professionals to brachytherapy within Quebec and Canada is essential to provide all patients the same access to this life saving modality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND AND PURPOSE: Despite the excellent clinical outcomes from brachytherapy treatments compared with other modalities and the low associated costs, there have been reports of a decline in utilization of brachytherapy. The aim of this study was to investigate in detail the trend in utilization of brachytherapy in the province of Québec, Canada, from 2011 to 2019.
MATERIALS AND METHODS: All radiotherapy clinics in the province of Quebec, and among these the clinics that provide brachytherapy treatments, were identified. This observational retrospective cohort study involved analysis of data compiled by the Ministère de la Santé et des Services Sociaux du Québec for the period of 2011 to end of 2019 on all brachytherapy procedures performed in the province of Quebec. Time series graphs were used to describe the number of high dose rate (HDR) and low dose rate (LDR) brachytherapy treatments during the studied time period. Statistical analysis was conducted using R statistical software.
RESULTS: Between 2011 and 2019, 12 hospitals in the province of Québec provided radiotherapy treatments, and all of them offered brachytherapy services. The median annual number of brachytherapy sessions was 4413 (range 3930-4829). HDR brachytherapy represented over 90% of all brachytherapy treatments throughout the study period. Significant changes over time were observed in the number of treatments: at least 5% change was seen only for the two most common subtypes of brachytherapy, HDR interstitial and HDR intracavitary, with an increase of 9.6% and a decrease of 9.2%, respectively. The use of other subtypes of brachytherapy (HDR-plesiotherapy, LDR-interstitial, LDR-intracavitary, LDR-eye plaque) was stable between 2011 and 2019, with ≤ 2.5% variation.
CONCLUSION: This study demonstrates an overall steady use of brachytherapy between 2011 and 2019 in Quebec. Brachytherapy offers numerous advantages for the treatment of diverse cancer sites. Although more sophisticated external beam radiotherapy treatments have emerged in the last decades, the precision and cost-effectiveness of brachytherapy remain unbeaten. To ensure the continued use and availability of brachytherapy, governments must put in place policies and regulations to that effect. Training and exposure of future health care professionals to brachytherapy within Quebec and Canada is essential to provide all patients the same access to this life saving modality.
MATERIALS AND METHODS: All radiotherapy clinics in the province of Quebec, and among these the clinics that provide brachytherapy treatments, were identified. This observational retrospective cohort study involved analysis of data compiled by the Ministère de la Santé et des Services Sociaux du Québec for the period of 2011 to end of 2019 on all brachytherapy procedures performed in the province of Quebec. Time series graphs were used to describe the number of high dose rate (HDR) and low dose rate (LDR) brachytherapy treatments during the studied time period. Statistical analysis was conducted using R statistical software.
RESULTS: Between 2011 and 2019, 12 hospitals in the province of Québec provided radiotherapy treatments, and all of them offered brachytherapy services. The median annual number of brachytherapy sessions was 4413 (range 3930-4829). HDR brachytherapy represented over 90% of all brachytherapy treatments throughout the study period. Significant changes over time were observed in the number of treatments: at least 5% change was seen only for the two most common subtypes of brachytherapy, HDR interstitial and HDR intracavitary, with an increase of 9.6% and a decrease of 9.2%, respectively. The use of other subtypes of brachytherapy (HDR-plesiotherapy, LDR-interstitial, LDR-intracavitary, LDR-eye plaque) was stable between 2011 and 2019, with ≤ 2.5% variation.
CONCLUSION: This study demonstrates an overall steady use of brachytherapy between 2011 and 2019 in Quebec. Brachytherapy offers numerous advantages for the treatment of diverse cancer sites. Although more sophisticated external beam radiotherapy treatments have emerged in the last decades, the precision and cost-effectiveness of brachytherapy remain unbeaten. To ensure the continued use and availability of brachytherapy, governments must put in place policies and regulations to that effect. Training and exposure of future health care professionals to brachytherapy within Quebec and Canada is essential to provide all patients the same access to this life saving modality.
Bui, Alaina; Childress, Lilian; Sankey, Jack; Seuntjens, Jan; Enger, Shirin A.
Effect of Incoming Particle Energy and Ionization Cluster Size on the G-value of Hydrated Electrons Presentation
AAPM 63rd Annual Meeting, 25.07.2021.
@misc{Bui2021,
title = {Effect of Incoming Particle Energy and Ionization Cluster Size on the G-value of Hydrated Electrons},
author = {Alaina Bui and Lilian Childress and Jack Sankey and Jan Seuntjens and Shirin A. Enger},
year = {2021},
date = {2021-07-25},
howpublished = {AAPM 63rd Annual Meeting},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Bui, Alaina; Bekerat, Hamed; Enger, Shirin A.
Film measurements for verification of dose results in hydrated electron dosimetry Presentation
COMP Virtual Scientific Meeting, 25.06.2021.
@misc{Bui2021b,
title = {Film measurements for verification of dose results in hydrated electron dosimetry},
author = {Alaina Bui and Hamed Bekerat and Shirin A. Enger},
year = {2021},
date = {2021-06-25},
howpublished = {COMP Virtual Scientific Meeting},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Bui, Alaina; Childress, Lilian; Sankey, Jack; Seuntjens, Jan; Enger, Shirin A.
Effect of incoming particle energy, cluster size, LET, and depth in water on the G-value of hydrated electrons Presentation
COMP Virtual Scientific Meeting, 22.06.2021.
@misc{Bui2021c,
title = {Effect of incoming particle energy, cluster size, LET, and depth in water on the G-value of hydrated electrons},
author = {Alaina Bui and Lilian Childress and Jack Sankey and Jan Seuntjens and Shirin A. Enger },
year = {2021},
date = {2021-06-22},
urldate = {2021-06-22},
howpublished = {COMP Virtual Scientific Meeting},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Weishaupt, Luca L.; Thibodeau-Antonacci, Alana; Garant, Aurelie; Singh, Kelita; Miller, Corey; Vuong, Té; Enger, Shirin A.
Inter-Observer Variability and Deep Learning in Rectal Tumor Segmentation from Endoscopy Images Presentation
The COMP Annual Scientific Meeting 2021, 22.06.2021.
@misc{Weishaupt2021c,
title = {Inter-Observer Variability and Deep Learning in Rectal Tumor Segmentation from Endoscopy Images},
author = {Luca L. Weishaupt and Alana Thibodeau-Antonacci and Aurelie Garant and Kelita Singh and Corey Miller and Té Vuong and Shirin A. Enger},
year = {2021},
date = {2021-06-22},
urldate = {2021-06-22},
abstract = {Purpose
To develop an automated rectal tumor segmentation algorithm from endoscopy images.
Material/Methods
A gastrointestinal physician (G1) segmented 2005 endoscopy images into tumor and non-tumor
regions. To quantify the inter-observer variability, a second gastrointestinal physician (G2)
contoured the images independently.
Three deep-learning architectures used for robust medical image segmentation in previous
studies were trained: a fully convolutional neural network (FCN32), a U-Net, and a SegNet.
Since the majority of the images did not contain tumors, two methods were compared for
training. Models were trained using only tumor images (M1) and all images (M2). G1’s images
and annotations were divided into 408 training, 82 validation, and 60 testing sets for M1, 1181
training, 372 validation, and 452 testing sets for M2.
Finally, segmentations from G2 and neural networks' predictions were compared against ground
truth labels from G1, and F1 scores were computed for images where both physicians found
tumors.
Results
The deep-learning segmentation took less than 1 second, while manual segmentation took
approximately 10 seconds per image.
The M1’s models consistently achieved equal or better scores (SegNet F1:0.80±0.08) than G2's
manual segmentations (F1:0.68±0.25). G2's low F1/DICE and precision scores indicate poor
agreement between the manual contours. Models from M2 achieved lower scores than G2 and
M1’s models since they demonstrated a strong bias towards predicting no tumor for all images.
Conclusion
Future studies will investigate training on an equal number of images with/without tumor, using
ground truth contours from multiple experts simultaneously.},
howpublished = {The COMP Annual Scientific Meeting 2021},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Purpose
To develop an automated rectal tumor segmentation algorithm from endoscopy images.
Material/Methods
A gastrointestinal physician (G1) segmented 2005 endoscopy images into tumor and non-tumor
regions. To quantify the inter-observer variability, a second gastrointestinal physician (G2)
contoured the images independently.
Three deep-learning architectures used for robust medical image segmentation in previous
studies were trained: a fully convolutional neural network (FCN32), a U-Net, and a SegNet.
Since the majority of the images did not contain tumors, two methods were compared for
training. Models were trained using only tumor images (M1) and all images (M2). G1’s images
and annotations were divided into 408 training, 82 validation, and 60 testing sets for M1, 1181
training, 372 validation, and 452 testing sets for M2.
Finally, segmentations from G2 and neural networks' predictions were compared against ground
truth labels from G1, and F1 scores were computed for images where both physicians found
tumors.
Results
The deep-learning segmentation took less than 1 second, while manual segmentation took
approximately 10 seconds per image.
The M1’s models consistently achieved equal or better scores (SegNet F1:0.80±0.08) than G2's
manual segmentations (F1:0.68±0.25). G2's low F1/DICE and precision scores indicate poor
agreement between the manual contours. Models from M2 achieved lower scores than G2 and
M1’s models since they demonstrated a strong bias towards predicting no tumor for all images.
Conclusion
Future studies will investigate training on an equal number of images with/without tumor, using
ground truth contours from multiple experts simultaneously.
To develop an automated rectal tumor segmentation algorithm from endoscopy images.
Material/Methods
A gastrointestinal physician (G1) segmented 2005 endoscopy images into tumor and non-tumor
regions. To quantify the inter-observer variability, a second gastrointestinal physician (G2)
contoured the images independently.
Three deep-learning architectures used for robust medical image segmentation in previous
studies were trained: a fully convolutional neural network (FCN32), a U-Net, and a SegNet.
Since the majority of the images did not contain tumors, two methods were compared for
training. Models were trained using only tumor images (M1) and all images (M2). G1’s images
and annotations were divided into 408 training, 82 validation, and 60 testing sets for M1, 1181
training, 372 validation, and 452 testing sets for M2.
Finally, segmentations from G2 and neural networks' predictions were compared against ground
truth labels from G1, and F1 scores were computed for images where both physicians found
tumors.
Results
The deep-learning segmentation took less than 1 second, while manual segmentation took
approximately 10 seconds per image.
The M1’s models consistently achieved equal or better scores (SegNet F1:0.80±0.08) than G2's
manual segmentations (F1:0.68±0.25). G2's low F1/DICE and precision scores indicate poor
agreement between the manual contours. Models from M2 achieved lower scores than G2 and
M1’s models since they demonstrated a strong bias towards predicting no tumor for all images.
Conclusion
Future studies will investigate training on an equal number of images with/without tumor, using
ground truth contours from multiple experts simultaneously.
Morcos, Marc; Antaki, Majd; Thibodeau-Antonacci, Alana; Kalinowski, Jonathan; Glickman, Harry; Enger, Shirin A.
RapidBrachyMCTPS: An open-source dose calculation and optimization tool for brachytherapy research Presentation
COMP, 01.06.2021.
@misc{Morcos2021c,
title = {RapidBrachyMCTPS: An open-source dose calculation and optimization tool for brachytherapy research},
author = {Marc Morcos and Majd Antaki and Alana Thibodeau-Antonacci and Jonathan Kalinowski and Harry Glickman and Shirin A. Enger},
year = {2021},
date = {2021-06-01},
howpublished = {COMP},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Thibodeau-Antonacci, Alana; Vuong, Té; Bekerat, Hamed; Liang, Liheng; Enger, Shirin A.
2021.
@award{Thibodeau-Antonacci2021b,
title = {Development of a Dynamic Shielding Intensity-Modulated Brachytherapy Applicator for the Treatment of Rectal Cancer},
author = {Alana Thibodeau-Antonacci and Té Vuong and Hamed Bekerat and Liheng Liang and Shirin A. Enger},
url = {https://curietherapi.es/},
year = {2021},
date = {2021-05-23},
urldate = {2021-05-23},
organization = {Curietherapies},
abstract = {Oral presentation given online at the annual congress of Curietherapies https://curietherapi.es/},
howpublished = {Annual Congress of Curietherapies},
keywords = {},
pubstate = {published},
tppubtype = {award}
}
Oral presentation given online at the annual congress of Curietherapies https://curietherapi.es/
Behmand, Behnaz; Evans, Michael D. C.; Kamio, Yuji; Enger, Shirin A.
Correlation between Radiation-induced Foci from 192Ir Brachytherapy and Tumor Nuclei Size Presentation
World Congress of Brachytherapy (WCB) - Online, 06.05.2021.
@misc{Behmand2021,
title = {Correlation between Radiation-induced Foci from 192Ir Brachytherapy and Tumor Nuclei Size},
author = {Behnaz Behmand and Michael D. C. Evans and Yuji Kamio and Shirin A. Enger},
year = {2021},
date = {2021-05-06},
howpublished = {World Congress of Brachytherapy (WCB) - Online},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Morcos, Marc; Viswanathan, Akila N.; Enger, Shirin A.
In: Medical Physics, vol. 48, no. 5, pp. 2604–2613, 2021, ISSN: 2473-4209.
@article{morcos_impact_2021,
title = {On the impact of absorbed dose specification, tissue heterogeneities, and applicator heterogeneities on Monte Carlo-based dosimetry of Ir-192, Se-75, and Yb-169 in conventional and intensity-modulated brachytherapy for the treatment of cervical cancer},
author = {Marc Morcos and Akila N. Viswanathan and Shirin A. Enger},
doi = {10.1002/mp.14802},
issn = {2473-4209},
year = {2021},
date = {2021-05-01},
journal = {Medical Physics},
volume = {48},
number = {5},
pages = {2604--2613},
abstract = {PURPOSE: The purpose of this study was to evaluate the impact of dose reporting schemes and tissue/applicator heterogeneities for 192 Ir-, 75 Se-, and 169 Yb-based MRI-guided conventional and intensity-modulated brachytherapy. METHODS AND MATERIALS: Treatment plans using a variety of dose reporting and tissue/applicator segmentation schemes were generated for a cohort (n = 10) of cervical cancer patients treated with 192 Ir-based Venezia brachytherapy. Dose calculations were performed using RapidBrachyMCTPS, a Geant4-based research Monte Carlo treatment planning system. Ultimately, five dose calculation scenarios were evaluated: (a) dose to water in water (Dw,w ); (b) Dw,w taking the applicator material into consideration (Dw,wApp ); (c) dose to water in medium (Dw,m ); (d and e) dose to medium in medium with mass densities assigned either nominally per structure (Dm,m (Nom) ) or voxel-by-voxel (Dm,m ).
RESULTS: Ignoring the plastic Venezia applicator (Dw,wApp ) overestimates Dm,m by up to 1% (average) with high energy source (192 Ir and 75 Se) and up to 2% with 169 Yb. Scoring dose to water (Dw,wApp or Dw,m ) generally overestimates dose and this effect increases with decreasing photon energy. Reporting dose other than Dm,m (or Dm,m Nom ) for 169 Yb-based conventional and intensity-modulated brachytherapy leads to a simultaneous overestimation (up to 4%) of CTVHR D90 and underestimation (up to 2%) of bladder D2cc due to a significant dip in the mass-energy absorption ratios at the depths of nearby targets and OARs. Using a nominal mass-density assignment per structure, rather than a CT-derived voxel-by-voxel assignment for MRI-guided brachytherapy, amounts to a dose error up to 1% for all radionuclides considered.
CONCLUSIONS: The effects of the considered dose reporting schemes trend correspondingly between conventional and intensity-modulated brachytherapy. In the absence of CT-derived mass densities, MRI-only-based dosimetry can adequately approximate Dm,m by assigning nominal mass densities to structures. Tissue and applicator heterogeneities do not significantly impact dosimetry for 192 Ir and 75 Se, but do for 169 Yb; dose reporting must be explicitly defined since Dw,m and Dw,w may overstate the dosimetric benefits.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
PURPOSE: The purpose of this study was to evaluate the impact of dose reporting schemes and tissue/applicator heterogeneities for 192 Ir-, 75 Se-, and 169 Yb-based MRI-guided conventional and intensity-modulated brachytherapy. METHODS AND MATERIALS: Treatment plans using a variety of dose reporting and tissue/applicator segmentation schemes were generated for a cohort (n = 10) of cervical cancer patients treated with 192 Ir-based Venezia brachytherapy. Dose calculations were performed using RapidBrachyMCTPS, a Geant4-based research Monte Carlo treatment planning system. Ultimately, five dose calculation scenarios were evaluated: (a) dose to water in water (Dw,w ); (b) Dw,w taking the applicator material into consideration (Dw,wApp ); (c) dose to water in medium (Dw,m ); (d and e) dose to medium in medium with mass densities assigned either nominally per structure (Dm,m (Nom) ) or voxel-by-voxel (Dm,m ).
RESULTS: Ignoring the plastic Venezia applicator (Dw,wApp ) overestimates Dm,m by up to 1% (average) with high energy source (192 Ir and 75 Se) and up to 2% with 169 Yb. Scoring dose to water (Dw,wApp or Dw,m ) generally overestimates dose and this effect increases with decreasing photon energy. Reporting dose other than Dm,m (or Dm,m Nom ) for 169 Yb-based conventional and intensity-modulated brachytherapy leads to a simultaneous overestimation (up to 4%) of CTVHR D90 and underestimation (up to 2%) of bladder D2cc due to a significant dip in the mass-energy absorption ratios at the depths of nearby targets and OARs. Using a nominal mass-density assignment per structure, rather than a CT-derived voxel-by-voxel assignment for MRI-guided brachytherapy, amounts to a dose error up to 1% for all radionuclides considered.
CONCLUSIONS: The effects of the considered dose reporting schemes trend correspondingly between conventional and intensity-modulated brachytherapy. In the absence of CT-derived mass densities, MRI-only-based dosimetry can adequately approximate Dm,m by assigning nominal mass densities to structures. Tissue and applicator heterogeneities do not significantly impact dosimetry for 192 Ir and 75 Se, but do for 169 Yb; dose reporting must be explicitly defined since Dw,m and Dw,w may overstate the dosimetric benefits.
RESULTS: Ignoring the plastic Venezia applicator (Dw,wApp ) overestimates Dm,m by up to 1% (average) with high energy source (192 Ir and 75 Se) and up to 2% with 169 Yb. Scoring dose to water (Dw,wApp or Dw,m ) generally overestimates dose and this effect increases with decreasing photon energy. Reporting dose other than Dm,m (or Dm,m Nom ) for 169 Yb-based conventional and intensity-modulated brachytherapy leads to a simultaneous overestimation (up to 4%) of CTVHR D90 and underestimation (up to 2%) of bladder D2cc due to a significant dip in the mass-energy absorption ratios at the depths of nearby targets and OARs. Using a nominal mass-density assignment per structure, rather than a CT-derived voxel-by-voxel assignment for MRI-guided brachytherapy, amounts to a dose error up to 1% for all radionuclides considered.
CONCLUSIONS: The effects of the considered dose reporting schemes trend correspondingly between conventional and intensity-modulated brachytherapy. In the absence of CT-derived mass densities, MRI-only-based dosimetry can adequately approximate Dm,m by assigning nominal mass densities to structures. Tissue and applicator heterogeneities do not significantly impact dosimetry for 192 Ir and 75 Se, but do for 169 Yb; dose reporting must be explicitly defined since Dw,m and Dw,w may overstate the dosimetric benefits.
Weishaupt, Luca L.; Torres, Jose; Camilleri-Broët, Sophie; Rayes, Roni F.; Spicer, Jonathan D.; Maldonado, Sabrina Côté; Enger, Shirin A.
Deep learning-based tumor segmentation on digital images of histopathology slides for microdosimetry applications Journal Article
In: arXiv:2105.01824 [physics], 2021, (arXiv: 2105.01824).
@article{weishaupt_deep_2021,
title = {Deep learning-based tumor segmentation on digital images of histopathology slides for microdosimetry applications},
author = {Luca L. Weishaupt and Jose Torres and Sophie Camilleri-Broët and Roni F. Rayes and Jonathan D. Spicer and Sabrina Côté Maldonado and Shirin A. Enger},
url = {http://arxiv.org/abs/2105.01824},
year = {2021},
date = {2021-05-01},
urldate = {2021-09-08},
journal = {arXiv:2105.01824 [physics]},
abstract = {$textbackslashbfPurpose:$ The goal of this study was (i) to use artificial intelligence to automate the traditionally labor-intensive process of manual segmentation of tumor regions in pathology slides performed by a pathologist and (ii) to validate the use of a well-known and readily available deep learning architecture. Automation will reduce the human error involved in manual delineation, increase efficiency, and result in accurate and reproducible segmentation. This advancement will alleviate the bottleneck in the workflow in clinical and research applications due to a lack of pathologist time. Our application is patient-specific microdosimetry and radiobiological modeling, which builds on the contoured pathology slides. $textbackslashbfMethods:$ A U-Net architecture was used to segment tumor regions in pathology core biopsies of lung tissue with adenocarcinoma stained using hematoxylin and eosin. A pathologist manually contoured the tumor regions in 56 images with binary masks for training. Overlapping patch extraction with various patch sizes and image downsampling were investigated individually. Data augmentation and 8-fold cross-validation were used. $textbackslashbfResults:$ The U-Net achieved accuracy of 0.91$textbackslashpm$0.06, specificity of 0.90$textbackslashpm$0.08, sensitivity of 0.92$textbackslashpm$0.07, and precision of 0.8$textbackslashpm$0.1. The F1/DICE score was 0.85$textbackslashpm$0.07, with a segmentation time of 3.24$textbackslashpm$0.03 seconds per image, achieving a 370$textbackslashpm$3 times increased efficiency over manual segmentation. In some cases, the U-Net correctly delineated the tumor's stroma from its epithelial component in regions that were classified as tumor by the pathologist. $textbackslashbfConclusion:$ The U-Net architecture can segment images with a level of efficiency and accuracy that makes it suitable for tumor segmentation of histopathological images in fields such as radiotherapy dosimetry, specifically in the subfields of microdosimetry.},
note = {arXiv: 2105.01824},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
$textbackslashbfPurpose:$ The goal of this study was (i) to use artificial intelligence to automate the traditionally labor-intensive process of manual segmentation of tumor regions in pathology slides performed by a pathologist and (ii) to validate the use of a well-known and readily available deep learning architecture. Automation will reduce the human error involved in manual delineation, increase efficiency, and result in accurate and reproducible segmentation. This advancement will alleviate the bottleneck in the workflow in clinical and research applications due to a lack of pathologist time. Our application is patient-specific microdosimetry and radiobiological modeling, which builds on the contoured pathology slides. $textbackslashbfMethods:$ A U-Net architecture was used to segment tumor regions in pathology core biopsies of lung tissue with adenocarcinoma stained using hematoxylin and eosin. A pathologist manually contoured the tumor regions in 56 images with binary masks for training. Overlapping patch extraction with various patch sizes and image downsampling were investigated individually. Data augmentation and 8-fold cross-validation were used. $textbackslashbfResults:$ The U-Net achieved accuracy of 0.91$textbackslashpm$0.06, specificity of 0.90$textbackslashpm$0.08, sensitivity of 0.92$textbackslashpm$0.07, and precision of 0.8$textbackslashpm$0.1. The F1/DICE score was 0.85$textbackslashpm$0.07, with a segmentation time of 3.24$textbackslashpm$0.03 seconds per image, achieving a 370$textbackslashpm$3 times increased efficiency over manual segmentation. In some cases, the U-Net correctly delineated the tumor's stroma from its epithelial component in regions that were classified as tumor by the pathologist. $textbackslashbfConclusion:$ The U-Net architecture can segment images with a level of efficiency and accuracy that makes it suitable for tumor segmentation of histopathological images in fields such as radiotherapy dosimetry, specifically in the subfields of microdosimetry.
Thibodeau-Antonacci, Alana; Vuong, Té; Bekerat, Hamed; Childress, Lilian; Enger, Shirin A.
OC-0112 development of a dynamic-shielding intensity modulated endorectal brachytherapy applicator Presentation
Radiotherapy and Oncology, 01.05.2021, ISBN: 0167-8140, 1879-0887.
@misc{Thibodeau-Antonacci2021,
title = {OC-0112 development of a dynamic-shielding intensity modulated endorectal brachytherapy applicator},
author = {Alana Thibodeau-Antonacci and Té Vuong and Hamed Bekerat and Lilian Childress and Shirin A. Enger},
url = {https://www.thegreenjournal.com/article/S0167-8140(21)06316-7/fulltext},
doi = {10.1016/S0167-8140(21)06316-7},
isbn = {0167-8140, 1879-0887},
year = {2021},
date = {2021-05-01},
abstract = {www.thegreenjournal.com},
howpublished = {Radiotherapy and Oncology},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Zou, Yujing; Lecavalier-Barsoum, Magali; Enger, Shirin A.
Treatment outcome Prediction for gynecological cancers patients with a machine learning model using pre/post diagnostic image modalities and digital histopathology images Presentation
CRUK RadNet Manchester AI for Optimising Radiotherapy Outcomes Workshop, 10.02.2021.
@misc{Zou2021,
title = {Treatment outcome Prediction for gynecological cancers patients with a machine learning model using pre/post diagnostic image modalities and digital histopathology images},
author = {Yujing Zou and Magali Lecavalier-Barsoum and Shirin A. Enger },
year = {2021},
date = {2021-02-10},
urldate = {2021-02-10},
abstract = {Oral Presentation (1 min fire-up pitch)},
howpublished = {CRUK RadNet Manchester AI for Optimising Radiotherapy Outcomes Workshop},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Oral Presentation (1 min fire-up pitch)
Morcos, Marc; Enger, Shirin A.
A novel minimally invasive IMBT delivery system for cervical cancer Presentation
JGH-Lady Davis Institute, 01.02.2021.
@misc{Morcos2021b,
title = {A novel minimally invasive IMBT delivery system for cervical cancer},
author = {Marc Morcos and Shirin A. Enger},
year = {2021},
date = {2021-02-01},
howpublished = {JGH-Lady Davis Institute},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Morcos, Marc; Antaki, Majd; Viswanathan, Akila N.; Enger, Shirin A.
A novel minimally invasive dynamic-shield, intensity-modulated brachytherapy system for the treatment of cervical cancer Journal Article
In: Medical Physics, vol. 48, no. 1, pp. 71–79, 2021, ISSN: 2473-4209.
@article{morcos_novel_2021,
title = {A novel minimally invasive dynamic-shield, intensity-modulated brachytherapy system for the treatment of cervical cancer},
author = {Marc Morcos and Majd Antaki and Akila N. Viswanathan and Shirin A. Enger},
doi = {10.1002/mp.14459},
issn = {2473-4209},
year = {2021},
date = {2021-01-01},
journal = {Medical Physics},
volume = {48},
number = {1},
pages = {71--79},
abstract = {PURPOSE: To present a novel, MRI-compatible dynamicshield intensity modulated brachytherapy (IMBT) applicator and delivery system using 192 Ir, 75 Se, and 169 Yb radioisotopes for the treatment of locally advanced cervical cancer. Needle-free IMBT is a promising technique for improving target coverage and organs at risk (OAR) sparing.
METHODS AND MATERIALS: The IMBT delivery system dynamically controls the rotation of a novel tungsten shield placed inside an MRI-compatible, 6-mm wide intrauterine tandem. Using 36 cervical cancer cases, conventional intracavitary brachytherapy (IC-BT) and intracavitary/interstitial brachytherapy (IC/IS-BT) (10Ci 192 Ir) plans were compared to IMBT (10Ci 192 Ir; 11.5Ci 75 Se; 44Ci 169 Yb). All plans were generated using the Geant4-based Monte Carlo dose calculation engine, RapidBrachyMC. Treatment plans were optimized then normalized to the same high-risk clinical target volume (HR-CTV) D90 and the D2cc for bladder, rectum, and sigmoid in the research brachytherapy planning system, RapidBrachyMCTPS. Plans were renormalized until either of the three OAR reached dose limits to calculate the maximum achievable HR-CTV D90 and D98 . RESULTS: Compared to IC-BT, IMBT with either of the three radionuclides significantly improves the HR-CTV D90 and D98 by up to 5.2% ± 0.3% (P textless 0.001) and 6.7% ± 0.5% (P textless 0.001), respectively, with the largest dosimetric enhancement when using 169 Yb followed by 75 Se and then 192 Ir. Similarly, D2cc for all OAR improved with IMBT by up to 7.7% ± 0.6% (P textless 0.001). For IC/IS-BT cases, needle-free IMBT achieved clinically acceptable plans with 169 Yb-based IMBT further improving HR-CTV D98 by 1.5% ± 0.2% (P = 0.034) and decreasing sigmoid D2cc by 1.9% ± 0.4% (P = 0.048). Delivery times for IMBT are increased by a factor of 1.7, 3.3, and 2.3 for 192 Ir, 75 Se, and 169 Yb, respectively, relative to conventional 192 Ir BT.
CONCLUSIONS: Dynamic shield IMBT provides a promising alternative to conventional IC- and IC/IS-BT techniques with significant dosimetric enhancements and even greater improvements with intermediate energy radionuclides. The ability to deliver a highly conformal, OAR-sparing dose without IS needles provides a simplified method for improving the therapeutic ratio less invasively and in a less resource intensive manner.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
PURPOSE: To present a novel, MRI-compatible dynamicshield intensity modulated brachytherapy (IMBT) applicator and delivery system using 192 Ir, 75 Se, and 169 Yb radioisotopes for the treatment of locally advanced cervical cancer. Needle-free IMBT is a promising technique for improving target coverage and organs at risk (OAR) sparing.
METHODS AND MATERIALS: The IMBT delivery system dynamically controls the rotation of a novel tungsten shield placed inside an MRI-compatible, 6-mm wide intrauterine tandem. Using 36 cervical cancer cases, conventional intracavitary brachytherapy (IC-BT) and intracavitary/interstitial brachytherapy (IC/IS-BT) (10Ci 192 Ir) plans were compared to IMBT (10Ci 192 Ir; 11.5Ci 75 Se; 44Ci 169 Yb). All plans were generated using the Geant4-based Monte Carlo dose calculation engine, RapidBrachyMC. Treatment plans were optimized then normalized to the same high-risk clinical target volume (HR-CTV) D90 and the D2cc for bladder, rectum, and sigmoid in the research brachytherapy planning system, RapidBrachyMCTPS. Plans were renormalized until either of the three OAR reached dose limits to calculate the maximum achievable HR-CTV D90 and D98 . RESULTS: Compared to IC-BT, IMBT with either of the three radionuclides significantly improves the HR-CTV D90 and D98 by up to 5.2% ± 0.3% (P textless 0.001) and 6.7% ± 0.5% (P textless 0.001), respectively, with the largest dosimetric enhancement when using 169 Yb followed by 75 Se and then 192 Ir. Similarly, D2cc for all OAR improved with IMBT by up to 7.7% ± 0.6% (P textless 0.001). For IC/IS-BT cases, needle-free IMBT achieved clinically acceptable plans with 169 Yb-based IMBT further improving HR-CTV D98 by 1.5% ± 0.2% (P = 0.034) and decreasing sigmoid D2cc by 1.9% ± 0.4% (P = 0.048). Delivery times for IMBT are increased by a factor of 1.7, 3.3, and 2.3 for 192 Ir, 75 Se, and 169 Yb, respectively, relative to conventional 192 Ir BT.
CONCLUSIONS: Dynamic shield IMBT provides a promising alternative to conventional IC- and IC/IS-BT techniques with significant dosimetric enhancements and even greater improvements with intermediate energy radionuclides. The ability to deliver a highly conformal, OAR-sparing dose without IS needles provides a simplified method for improving the therapeutic ratio less invasively and in a less resource intensive manner.
METHODS AND MATERIALS: The IMBT delivery system dynamically controls the rotation of a novel tungsten shield placed inside an MRI-compatible, 6-mm wide intrauterine tandem. Using 36 cervical cancer cases, conventional intracavitary brachytherapy (IC-BT) and intracavitary/interstitial brachytherapy (IC/IS-BT) (10Ci 192 Ir) plans were compared to IMBT (10Ci 192 Ir; 11.5Ci 75 Se; 44Ci 169 Yb). All plans were generated using the Geant4-based Monte Carlo dose calculation engine, RapidBrachyMC. Treatment plans were optimized then normalized to the same high-risk clinical target volume (HR-CTV) D90 and the D2cc for bladder, rectum, and sigmoid in the research brachytherapy planning system, RapidBrachyMCTPS. Plans were renormalized until either of the three OAR reached dose limits to calculate the maximum achievable HR-CTV D90 and D98 . RESULTS: Compared to IC-BT, IMBT with either of the three radionuclides significantly improves the HR-CTV D90 and D98 by up to 5.2% ± 0.3% (P textless 0.001) and 6.7% ± 0.5% (P textless 0.001), respectively, with the largest dosimetric enhancement when using 169 Yb followed by 75 Se and then 192 Ir. Similarly, D2cc for all OAR improved with IMBT by up to 7.7% ± 0.6% (P textless 0.001). For IC/IS-BT cases, needle-free IMBT achieved clinically acceptable plans with 169 Yb-based IMBT further improving HR-CTV D98 by 1.5% ± 0.2% (P = 0.034) and decreasing sigmoid D2cc by 1.9% ± 0.4% (P = 0.048). Delivery times for IMBT are increased by a factor of 1.7, 3.3, and 2.3 for 192 Ir, 75 Se, and 169 Yb, respectively, relative to conventional 192 Ir BT.
CONCLUSIONS: Dynamic shield IMBT provides a promising alternative to conventional IC- and IC/IS-BT techniques with significant dosimetric enhancements and even greater improvements with intermediate energy radionuclides. The ability to deliver a highly conformal, OAR-sparing dose without IS needles provides a simplified method for improving the therapeutic ratio less invasively and in a less resource intensive manner.
DeCunha, Joseph M.; Poole, Christopher M.; Vallières, Martin; Torres, Jose; Camilleri-Broët, Sophie; Rayes, Roni F.; Spicer, Jonathan D.; Enger, Shirin A.
Development of patient-specific 3D models from histopathological samples for applications in radiation therapy Journal Article
In: Physica medica: PM: an international journal devoted to the applications of physics to medicine and biology: official journal of the Italian Association of Biomedical Physics (AIFB), vol. 81, pp. 162–169, 2021, ISSN: 1724-191X.
@article{decunha_development_2021,
title = {Development of patient-specific 3D models from histopathological samples for applications in radiation therapy},
author = {Joseph M. DeCunha and Christopher M. Poole and Martin Vallières and Jose Torres and Sophie Camilleri-Broët and Roni F. Rayes and Jonathan D. Spicer and Shirin A. Enger},
doi = {10.1016/j.ejmp.2020.12.009},
issn = {1724-191X},
year = {2021},
date = {2021-01-01},
journal = {Physica medica: PM: an international journal devoted to the applications of physics to medicine and biology: official journal of the Italian Association of Biomedical Physics (AIFB)},
volume = {81},
pages = {162--169},
abstract = {The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data. The aim of this study was to develop methods to derive cell/nucleus size distributions from digital images of 2D histopathological samples and use them to build digital 3D models for use in cellular dosimetry. Nineteen of sixty hematoxylin and eosin stained lung adenocarcinoma samples investigated passed exclusion criterion to be analyzed in the study. A difference of gaussians blob detection algorithm was used to identify nucleus centers and quantify cell spacing. Hematoxylin content was measured to determine nucleus radius. Pouring simulations were conducted to generate one-hundred 3D models containing volumes of equivalent cell spacing and nuclei radius to those in histopathological samples. The nuclei radius distributions of non-tumoral and cancerous regions appearing in the same slide were significantly different (p textless 0.01) in all samples analyzed. The median nuclear-cytoplasmic ratio was 0.36 for non-tumoral cells and 0.50 for cancerous cells. The average cellular and nucleus packing densities in the 3D models generated were 65.9% (SD: 1.5%) and 13.3% (SD: 0.3%) respectively. Software to determine cell spacing and nuclei radius from histopathological samples was developed. 3D digital tissue models containing volumes with equivalent cell spacing, nucleus radius, and packing density to cancerous tissues were generated.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data. The aim of this study was to develop methods to derive cell/nucleus size distributions from digital images of 2D histopathological samples and use them to build digital 3D models for use in cellular dosimetry. Nineteen of sixty hematoxylin and eosin stained lung adenocarcinoma samples investigated passed exclusion criterion to be analyzed in the study. A difference of gaussians blob detection algorithm was used to identify nucleus centers and quantify cell spacing. Hematoxylin content was measured to determine nucleus radius. Pouring simulations were conducted to generate one-hundred 3D models containing volumes of equivalent cell spacing and nuclei radius to those in histopathological samples. The nuclei radius distributions of non-tumoral and cancerous regions appearing in the same slide were significantly different (p textless 0.01) in all samples analyzed. The median nuclear-cytoplasmic ratio was 0.36 for non-tumoral cells and 0.50 for cancerous cells. The average cellular and nucleus packing densities in the 3D models generated were 65.9% (SD: 1.5%) and 13.3% (SD: 0.3%) respectively. Software to determine cell spacing and nuclei radius from histopathological samples was developed. 3D digital tissue models containing volumes with equivalent cell spacing, nucleus radius, and packing density to cancerous tissues were generated.
DeCunha, Joseph M.; Villegas, Fernanda; Vallières, Martin; Torres, Jose; Camilleri-Broët, Sophie; Enger, Shirin A.
Patient-specific microdosimetry: a proof of concept Journal Article
In: Physics in Medicine & Biology, 2021, ISSN: 0031-9155.
@article{decunha_patient-specific_2021b,
title = {Patient-specific microdosimetry: a proof of concept},
author = {Joseph M. DeCunha and Fernanda Villegas and Martin Vallières and Jose Torres and Sophie Camilleri-Broët and Shirin A. Enger},
url = {http://iopscience.iop.org/article/10.1088/1361-6560/ac1d1e},
doi = {10.1088/1361-6560/ac1d1e},
issn = {0031-9155},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Physics in Medicine & Biology},
abstract = {Microscopic energy deposition distributions from ionizing radiation are used to predict the biological effects of an irradiation and vary depending on biological target size. Ionizing radiation is thought to kill cells or inhibit cell cycling mainly by damaging DNA in the cell nucleus. The size of cells and nuclei depends on tissue type, cell cycle, and malignancy, all of which vary between patients. The aim of this study was to develop methods to perform patient-specific microdosimetry, that being, determining microdosimetric quantities in volumes that correspond to the sizes of cells and nuclei observed in a patient’s tissue. A histopathological sample extracted from a stage I lung adenocarcinoma patient was analyzed. A pouring simulation was used to generate a three-dimensional tissue model from cell and nucleus size information determined from the histopathological sample. Microdosimetric distributions including f(y) and d(y) were determined for Co-60,Ir-192,Yb-169 and I-125 in a patient-specific model containing a distribution of cell and nucleus sizes. Fixed radius models and a summation method (where f(y) from many fixed radii models are summed) were compared to the full patient-specific model to evaluate their suitability for fast determination of patient-specific microdosimetric parameters. Fixed radius models do not provide a close approximation of the full patient-specific model y ̅_f or y ̅_d for the lower energy sources investigated, Yb-169 and I-125. The higher energy sources investigated, Co-60 and Ir-192 are less sensitive to target size variation than Yb-169 and I-125. A summation method yields the most accurate approximation of the full model d(y) for all radioisotopes investigated. A summation method allows for the computation of patient-specific microdosimetric distributions with the computing power of a personal computer. With appropriate biological inputs the microdosimetric distributions computed using these methods can yield a patient-specific relative biological effectiveness as part of a multiscale treatment planning approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Microscopic energy deposition distributions from ionizing radiation are used to predict the biological effects of an irradiation and vary depending on biological target size. Ionizing radiation is thought to kill cells or inhibit cell cycling mainly by damaging DNA in the cell nucleus. The size of cells and nuclei depends on tissue type, cell cycle, and malignancy, all of which vary between patients. The aim of this study was to develop methods to perform patient-specific microdosimetry, that being, determining microdosimetric quantities in volumes that correspond to the sizes of cells and nuclei observed in a patient’s tissue. A histopathological sample extracted from a stage I lung adenocarcinoma patient was analyzed. A pouring simulation was used to generate a three-dimensional tissue model from cell and nucleus size information determined from the histopathological sample. Microdosimetric distributions including f(y) and d(y) were determined for Co-60,Ir-192,Yb-169 and I-125 in a patient-specific model containing a distribution of cell and nucleus sizes. Fixed radius models and a summation method (where f(y) from many fixed radii models are summed) were compared to the full patient-specific model to evaluate their suitability for fast determination of patient-specific microdosimetric parameters. Fixed radius models do not provide a close approximation of the full patient-specific model y ̅_f or y ̅_d for the lower energy sources investigated, Yb-169 and I-125. The higher energy sources investigated, Co-60 and Ir-192 are less sensitive to target size variation than Yb-169 and I-125. A summation method yields the most accurate approximation of the full model d(y) for all radioisotopes investigated. A summation method allows for the computation of patient-specific microdosimetric distributions with the computing power of a personal computer. With appropriate biological inputs the microdosimetric distributions computed using these methods can yield a patient-specific relative biological effectiveness as part of a multiscale treatment planning approach.
Weishaupt, Luca L.; Sayed, Hisham Kamal; Mao, Ximeng; Choo, Chunhee; Stish, Bradley; Enger, Shirin A.; Deufel, Christopher
Approaching automated applicator digitization from a new angle: using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy Journal Article
In: 2021 ABS Annual Meeting, 2021, (Type: Journal Article).
@article{weishaupt_approaching_2021-1,
title = {Approaching automated applicator digitization from a new angle: using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy},
author = {Luca L. Weishaupt and Hisham Kamal Sayed and Ximeng Mao and Chunhee Choo and Bradley Stish and Shirin A. Enger and Christopher Deufel},
year = {2021},
date = {2021-01-01},
journal = {2021 ABS Annual Meeting},
note = {Type: Journal Article},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Morcos, Marc; Viswanathan, Akila N.; Enger, Shirin A.
In: Medical Physics, vol. 48, no. 5, pp. 2604–2613, 2021, ISSN: 2473-4209, (_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14802).
@article{morcos_impact_2021b,
title = {On the impact of absorbed dose specification, tissue heterogeneities, and applicator heterogeneities on Monte Carlo-based dosimetry of Ir-192, Se-75, and Yb-169 in conventional and intensity-modulated brachytherapy for the treatment of cervical cancer},
author = {Marc Morcos and Akila N. Viswanathan and Shirin A. Enger},
url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.14802},
doi = {10.1002/mp.14802},
issn = {2473-4209},
year = {2021},
date = {2021-01-01},
urldate = {2021-09-08},
journal = {Medical Physics},
volume = {48},
number = {5},
pages = {2604--2613},
abstract = {Purpose The purpose of this study was to evaluate the impact of dose reporting schemes and tissue/applicator heterogeneities for 192Ir-, 75Se-, and 169Yb-based MRI-guided conventional and intensity-modulated brachytherapy. Methods and Materials Treatment plans using a variety of dose reporting and tissue/applicator segmentation schemes were generated for a cohort (n = 10) of cervical cancer patients treated with 192Ir-based Venezia brachytherapy. Dose calculations were performed using RapidBrachyMCTPS, a Geant4-based research Monte Carlo treatment planning system. Ultimately, five dose calculation scenarios were evaluated: (a) dose to water in water (Dw,w); (b) Dw,w taking the applicator material into consideration (Dw,wApp); (c) dose to water in medium (Dw,m); (d and e) dose to medium in medium with mass densities assigned either nominally per structure (Dm,m (Nom)) or voxel-by-voxel (Dm,m). Results Ignoring the plastic Venezia applicator (Dw,wApp) overestimates Dm,m by up to 1% (average) with high energy source (192Ir and 75Se) and up to 2% with 169Yb. Scoring dose to water (Dw,wApp or Dw,m) generally overestimates dose and this effect increases with decreasing photon energy. Reporting dose other than Dm,m (or Dm,m Nom) for 169Yb-based conventional and intensity-modulated brachytherapy leads to a simultaneous overestimation (up to 4%) of CTVHR D90 and underestimation (up to 2%) of bladder D2cc due to a significant dip in the mass-energy absorption ratios at the depths of nearby targets and OARs. Using a nominal mass-density assignment per structure, rather than a CT-derived voxel-by-voxel assignment for MRI-guided brachytherapy, amounts to a dose error up to 1% for all radionuclides considered. Conclusions The effects of the considered dose reporting schemes trend correspondingly between conventional and intensity-modulated brachytherapy. In the absence of CT-derived mass densities, MRI-only-based dosimetry can adequately approximate Dm,m by assigning nominal mass densities to structures. Tissue and applicator heterogeneities do not significantly impact dosimetry for 192Ir and 75Se, but do for 169Yb; dose reporting must be explicitly defined since Dw,m and Dw,w may overstate the dosimetric benefits.},
note = {_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14802},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose The purpose of this study was to evaluate the impact of dose reporting schemes and tissue/applicator heterogeneities for 192Ir-, 75Se-, and 169Yb-based MRI-guided conventional and intensity-modulated brachytherapy. Methods and Materials Treatment plans using a variety of dose reporting and tissue/applicator segmentation schemes were generated for a cohort (n = 10) of cervical cancer patients treated with 192Ir-based Venezia brachytherapy. Dose calculations were performed using RapidBrachyMCTPS, a Geant4-based research Monte Carlo treatment planning system. Ultimately, five dose calculation scenarios were evaluated: (a) dose to water in water (Dw,w); (b) Dw,w taking the applicator material into consideration (Dw,wApp); (c) dose to water in medium (Dw,m); (d and e) dose to medium in medium with mass densities assigned either nominally per structure (Dm,m (Nom)) or voxel-by-voxel (Dm,m). Results Ignoring the plastic Venezia applicator (Dw,wApp) overestimates Dm,m by up to 1% (average) with high energy source (192Ir and 75Se) and up to 2% with 169Yb. Scoring dose to water (Dw,wApp or Dw,m) generally overestimates dose and this effect increases with decreasing photon energy. Reporting dose other than Dm,m (or Dm,m Nom) for 169Yb-based conventional and intensity-modulated brachytherapy leads to a simultaneous overestimation (up to 4%) of CTVHR D90 and underestimation (up to 2%) of bladder D2cc due to a significant dip in the mass-energy absorption ratios at the depths of nearby targets and OARs. Using a nominal mass-density assignment per structure, rather than a CT-derived voxel-by-voxel assignment for MRI-guided brachytherapy, amounts to a dose error up to 1% for all radionuclides considered. Conclusions The effects of the considered dose reporting schemes trend correspondingly between conventional and intensity-modulated brachytherapy. In the absence of CT-derived mass densities, MRI-only-based dosimetry can adequately approximate Dm,m by assigning nominal mass densities to structures. Tissue and applicator heterogeneities do not significantly impact dosimetry for 192Ir and 75Se, but do for 169Yb; dose reporting must be explicitly defined since Dw,m and Dw,w may overstate the dosimetric benefits.
Morcos, Marc; Antaki, Majd; Viswanathan, Akila N.; Enger, Shirin A.
ESTRO Newsletter, 2021.
@misc{nokey,
title = {A novel, minimally invasive, dynamic‐shield, intensity‐modulated brachytherapy system for the treatment of cervical cancer. Editors’ pick.},
author = {Marc Morcos and Majd Antaki and Akila N. Viswanathan and Shirin A. Enger},
url = {https://www.estro.org/About/Newsroom/Newsletter/Brachytheraphy/A-novel,-minimally-invasive,-dynamic%E2%80%90shield,-inten },
year = {2021},
date = {2021-01-01},
howpublished = {ESTRO Newsletter},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Morcos, Marc; Enger, Shirin A.
MR-guided intensity modulated brachytherapy for gynecologic cancers Presentation
McGill FMT, 01.01.2021.
@misc{Morcos2021,
title = {MR-guided intensity modulated brachytherapy for gynecologic cancers},
author = {Marc Morcos and Shirin A. Enger},
year = {2021},
date = {2021-01-01},
howpublished = {McGill FMT},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
2020
Antaki, Majd; Deufel, Christopher L; Enger, Shirin A.
Fast mixed integer optimization (FMIO) for high dose rate brachytherapy Journal Article
In: Physics in Medicine and Biology, vol. 65, no. 21, pp. 215005, 2020, ISSN: 1361-6560.
@article{antaki_fast_2020,
title = {Fast mixed integer optimization (FMIO) for high dose rate brachytherapy},
author = {Majd Antaki and Christopher L Deufel and Shirin A. Enger},
doi = {10.1088/1361-6560/aba317},
issn = {1361-6560},
year = {2020},
date = {2020-12-01},
journal = {Physics in Medicine and Biology},
volume = {65},
number = {21},
pages = {215005},
abstract = {The purpose of this work was to develop an efficient quadratic mixed integer programming algorithm for high dose rate (HDR) brachytherapy treatment planning problems and integrate the algorithm into an open-source Monte Carlo based treatment planning software, RapidBrachyMCTPS. The mixed-integer algorithm yields a globally optimum solution to the dose volume histogram (DVH) based problem and, unlike other methods, is not susceptible to local minimum trapping. A hybrid linear-quadratic penalty model coupled to a mixed integer programming model was used to optimize treatment plans for 10 prostate cancer patients. Dose distributions for each dwell position were calculated with RapidBrachyMCTPS with type A uncertainties less than 0.2% in voxels within the planning target volume (PTV). The optimization process was divided into two parts. First, the data was preprocessed, in which the problem size was reduced by eliminating voxels that had negligible impact on the solution (e.g. far from the dwell position). Second, the best combination of dwell times to obtain a plan with the highest score was found. The dwell positions and dose volume constraints were used as input to a commercial mixed integer optimizer (Gurobi Optimization, Inc.). A penalty-based criterion was adopted for the scoring. The voxel-reduction technique successfully reduced the problem size by an average of 91%, without loss of quality. The preprocessing of the optimization process required on average 4 s and solving for the global maximum required on average 33 s. The total optimization time averaged 37 s, which is a substantial improvement over the ∼15 min optimization time reported in published literature. The plan quality was evaluated by evaluating dose volume metrics, including PTV D90, rectum and bladder D1cc and urethra D0.1cc. In conclusion, fast mixed integer optimization is an order of magnitude faster than current mixed-integer approaches for solving HDR brachytherapy treatment planning problems with DVH based metrics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The purpose of this work was to develop an efficient quadratic mixed integer programming algorithm for high dose rate (HDR) brachytherapy treatment planning problems and integrate the algorithm into an open-source Monte Carlo based treatment planning software, RapidBrachyMCTPS. The mixed-integer algorithm yields a globally optimum solution to the dose volume histogram (DVH) based problem and, unlike other methods, is not susceptible to local minimum trapping. A hybrid linear-quadratic penalty model coupled to a mixed integer programming model was used to optimize treatment plans for 10 prostate cancer patients. Dose distributions for each dwell position were calculated with RapidBrachyMCTPS with type A uncertainties less than 0.2% in voxels within the planning target volume (PTV). The optimization process was divided into two parts. First, the data was preprocessed, in which the problem size was reduced by eliminating voxels that had negligible impact on the solution (e.g. far from the dwell position). Second, the best combination of dwell times to obtain a plan with the highest score was found. The dwell positions and dose volume constraints were used as input to a commercial mixed integer optimizer (Gurobi Optimization, Inc.). A penalty-based criterion was adopted for the scoring. The voxel-reduction technique successfully reduced the problem size by an average of 91%, without loss of quality. The preprocessing of the optimization process required on average 4 s and solving for the global maximum required on average 33 s. The total optimization time averaged 37 s, which is a substantial improvement over the ∼15 min optimization time reported in published literature. The plan quality was evaluated by evaluating dose volume metrics, including PTV D90, rectum and bladder D1cc and urethra D0.1cc. In conclusion, fast mixed integer optimization is an order of magnitude faster than current mixed-integer approaches for solving HDR brachytherapy treatment planning problems with DVH based metrics.
Bui, Alaina; Childress, Lilian; Sankey, Jack; Enger, Shirin A.
Developing a hydrated electron dosimeter and determining the G-value of hydrated electrons Presentation
Association Québécoise des Physicien(ne)s Médicaux Cliniques, 05.11.2020.
@misc{Bui2020,
title = {Developing a hydrated electron dosimeter and determining the G-value of hydrated electrons},
author = {Alaina Bui and Lilian Childress and Jack Sankey and Shirin A. Enger},
year = {2020},
date = {2020-11-05},
howpublished = {Association Québécoise des Physicien(ne)s Médicaux Cliniques},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
