Publications • EngerLab

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2022

Liam Carroll, Shirin A. Enger

Simulation of a novel, non-invasive radiation detector to measure the arterial input function for dynamic PET Journal Article

In: Medical Physics , 2022.

Abstract | Links | BibTeX | Tags:

@article{nokey,

title = {Simulation of a novel, non-invasive radiation detector to measure the arterial input function for dynamic PET},

author = {Liam Carroll, Shirin A. Enger},

url = {https://doi-org.proxy3.library.mcgill.ca/10.1002/mp.16055

doi = {10.1002/mp.16055},

year = {2022},

date = {2022-10-17},

journal = {Medical Physics },

abstract = {Background:

Dynamic positron emission tomography (dPET) is a nuclear medicine imaging technique providing functional images for organs of interest with applications in oncology, cardiology, and drug discovery. This technique requires the acquisition of the time-course arterial plasma activity concentration, called the arterial input function (AIF), which is conventionally acquired via arterial blood sampling.

Purpose:

The aim of this study was to A) optimize the geometry for a novel and cost efficient non-invasive detector called NID designed to measure the AIF for dPET scans through Monte Carlo simulations and B) develop a clinical data analysis chain to successfully separate the arterial component of a simulated AIF signal from the venous component.

Methods:

The NID was optimized by using an in-house Geant4-based software package. The sensitive volume of the NID consists of a band of 10 cm long and 1 mm in diameter scintillating fibers placed over a wrist phantom. The phantom was simulated as a cylinder, 10 cm long and 6.413 cm in diameter comprised of polyethylene with two holes placed through it to simulate the patient's radial artery and vein. This phantom design was chosen to match the wrist phantom used in our previous proof of concept work. Two geometries were simulated with different arrangements of scintillating fibers. The first design used a single layer of 64 fibers. The second used two layers, an inner layer with 29 fibers and an outer layer with 30 fibers. Four positron emitting radioisotopes were simulated: 18F, 11C, 15O and 68Ga with 100 million simulated decay events per run. The total and intrinsic efficiencies of both designs were calculated as well as the full width half max (FWHM) of the signal. In addition, contribution by the annihilation photons vs positrons to the signal was investigated. The results obtained from the two simulated detector models were compared. A clinical data analysis chain using an expectation maximization maximum likelihood algorithm was tested. This analysis chain will be used to separate arterial counts from the total signal.

Results:

The second NID design with two layers of scintillating fibers had a higher efficiency for all simulations with a maximum increase of 17% total efficiency for 11C simulation. All simulations had a significant annihilation photon contribution. The signal for 18F and 11C was almost entirely due to photons. The clinical data analysis chain was within 1% of the true value for 434 out of 440 trials. Further experimental studies to validate these simulations will be required.

Conclusions:

The design of the NID was optimized and its efficiency increased through Monte Carlo simulations. A clinical data analysis chain was successfully developed to separate the arterial component of an AIF signal from the venous component. The simulations show that the NID can be used to accurately measure the AIF non-invasively for dPET scans.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Background:
Dynamic positron emission tomography (dPET) is a nuclear medicine imaging technique providing functional images for organs of interest with applications in oncology, cardiology, and drug discovery. This technique requires the acquisition of the time-course arterial plasma activity concentration, called the arterial input function (AIF), which is conventionally acquired via arterial blood sampling.

Purpose:
The aim of this study was to A) optimize the geometry for a novel and cost efficient non-invasive detector called NID designed to measure the AIF for dPET scans through Monte Carlo simulations and B) develop a clinical data analysis chain to successfully separate the arterial component of a simulated AIF signal from the venous component.

Methods:
The NID was optimized by using an in-house Geant4-based software package. The sensitive volume of the NID consists of a band of 10 cm long and 1 mm in diameter scintillating fibers placed over a wrist phantom. The phantom was simulated as a cylinder, 10 cm long and 6.413 cm in diameter comprised of polyethylene with two holes placed through it to simulate the patient's radial artery and vein. This phantom design was chosen to match the wrist phantom used in our previous proof of concept work. Two geometries were simulated with different arrangements of scintillating fibers. The first design used a single layer of 64 fibers. The second used two layers, an inner layer with 29 fibers and an outer layer with 30 fibers. Four positron emitting radioisotopes were simulated: 18F, 11C, 15O and 68Ga with 100 million simulated decay events per run. The total and intrinsic efficiencies of both designs were calculated as well as the full width half max (FWHM) of the signal. In addition, contribution by the annihilation photons vs positrons to the signal was investigated. The results obtained from the two simulated detector models were compared. A clinical data analysis chain using an expectation maximization maximum likelihood algorithm was tested. This analysis chain will be used to separate arterial counts from the total signal.

Results:
The second NID design with two layers of scintillating fibers had a higher efficiency for all simulations with a maximum increase of 17% total efficiency for 11C simulation. All simulations had a significant annihilation photon contribution. The signal for 18F and 11C was almost entirely due to photons. The clinical data analysis chain was within 1% of the true value for 434 out of 440 trials. Further experimental studies to validate these simulations will be required.

Conclusions:
The design of the NID was optimized and its efficiency increased through Monte Carlo simulations. A clinical data analysis chain was successfully developed to separate the arterial component of an AIF signal from the venous component. The simulations show that the NID can be used to accurately measure the AIF non-invasively for dPET scans.

Sebastien, Quetin

Artificial Intelligence-based Brachytherapy Presentation

From New avenues in the non-operative management of patients with rectal cancer Conference, 14.10.2022.

BibTeX | Tags:

Zou, Yujing

TransMedTech Excellence Scholarship (Doctoral Award) award

2022.

Links | BibTeX | Tags:

Kalinowski, Jonathan

McGill Faculty of Medicine and Health Sciences Internal Studentship award

2022.

Abstract | Links | BibTeX | Tags:

Rahbaran, Maryam

Graduate Excellence Award award

2022.

Abstract | BibTeX | Tags:

Sebastien, Quetin; Zou, Yujing

Deep Learning Framework : Tensorboard and Pytorch Lightning Workshop

2022.

Links | BibTeX | Tags:

Zou, Yujing; Alvarez, David-Santiago Ayala

ESTRO 2022: innovations in brachytherapy Journal Article

In: ESTRO Newsletter, pp. 754–767, 2022.

Links | BibTeX | Tags:

Sebastien, Quetin

Deep Learning Framework : Pytorch tensors and Autograd Workshop

2022.

Links | BibTeX | Tags:

Sebastien, Quetin; Zou, Yujing

Introduction to medical image processing with Python : DICOM and histopathology images Workshop

2022.

Links | BibTeX | Tags:

Zou, Yujing

Fonds de recherche du Québec - Santé (FRQS) PhD doctoral training scholarship award

2022, ($84,000 for 2022 - 2026).

Links | BibTeX | Tags:

Zou, Yujing

Fonds de recherche du Québec - Nature et technologies (FRQNT) PhD doctoral training scholarship award

2022, ($84,000 for 2022 - 2026; Declined due to acceptance of FRQS).

Links | BibTeX | Tags:

Zou, Yujing; Lecavalier-Barsoum, Magali; Pelmus, Manuela; Maleki, Farhad; Enger, Shirin A.

Predictive modeling of post radiation-therapy recurrence for gynecological cancer patients using clinical and histopathology imaging features Conference

Curietherapies 2022.

Abstract | Links | BibTeX | Tags:

@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.},

keywords = {},

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.

Abstract | Links | BibTeX | Tags:

@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.

Zou, Yujing

Biological & Biomedical Engineering PhD Recruitment Award award

2022, (These awards are designed to help recruit top students to our program and are offered to applicants wishing to start in Fall or Winter. The standard recruitment awards are $10,000/year for three years for Doctoral students.).

Links | BibTeX | Tags:

Jafarzadeh, Hossein

Biological & Biomedical Engineering PhD Recruitment Award award

2022.

Links | BibTeX | Tags:

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).

BibTeX | Tags:

Zou, Yujing

Graduate Research Enhancement and Travel Awards (GREAT Awards) award

2022, (In 2009, Graduate and Postdoctoral Studies introduced the Graduate Research Enhancement and Travel awards (GREAT awards) program in consultation with the Faculty Deans and Associate Deans. These awards cover dissemination of research through graduate student presentations at conferences, and other graduate student research-enhancement activities, such as travel for fieldwork, archival inquiry and extra-mural collaborative research. GREAT budgets are allocated to Faculties each year, and as such, are managed directly by the Associate Dean's office.).

Links | BibTeX | Tags:

Carroll, Liam; Enger, Shirin A.

Second prize at the International Conference on Monte Carlo Techniques for Medical Applications award

2022.

Abstract | BibTeX | Tags:

@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.

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.

Abstract | BibTeX | Tags:

@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."},

keywords = {},

pubstate = {published},

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."

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.

Abstract | BibTeX | Tags:

@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).
"

Enger, Shirin A.; Sankey, Jack; Childress, Lilian; Megroureche, Julien

Radiation dosimeter Patent

2022, (US Patent App. 17/298,743).

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

Zou, Yujing; Lecavalier-barsoum, Magali; Pelmus, Manuela; Enger, Shirin A.

Patient-Specific Nuclei Size and Cell Spacing Distribution Extraction From Histopathology Whole Slide Images for Treatment Outcome Prediction Modelling Inproceedings

In: MEDICAL PHYSICS, pp. E266–E266, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.

Abstract | Links | BibTeX | Tags:

@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

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.

BibTeX | Tags:

Daoud, Youstina; Carroll, Liam; Enger, Shrin A.

PO-1617 Mapping of the human wrist to develop a non-invasive radiation detector for Dynamic PET application Journal Article

In: Radiotherapy and Oncology, vol. 170, pp. S1405–S1406, 2022.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

Abstract | Links | BibTeX | Tags:

@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}

}

Enger, Shirin A.

SP-0706 Creating a'successful'work environment Journal Article

In: Radiotherapy and Oncology, vol. 170, pp. S620, 2022.

BibTeX | Tags:

Weishaupt, Luca L.; Sayed, Hisham Kamal; Mao, Ximeng; Choo, Richard; Stish, Bradley J; 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: Brachytherapy, 2022.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

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.

BibTeX | Tags:

Carroll, Liam; Enger, Shirin A

Simulation of a novel, non-invasive radiation detector to measure the arterial input function for dynamic PET Journal Article

In: Medical Physics, 2022.

BibTeX | Tags:

2021

Weishaupt, Luca L.

T I Gurman Prize in Physics award

2021.

Abstract | Links | BibTeX | Tags:

Thibodeau-Antonacci, Alana

Canada Graduate Scholarship – Doctoral Program award

2021.

Links | BibTeX | Tags:

Esmaelbeigi, Azin

Biological & Biomedical Engineering PhD Recruitment Award award

2021.

Links | BibTeX | Tags:

Kalinowski, Jonathan

Merit-based recruitment award for first year MSc students. award

2021.

BibTeX | Tags:

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.

Abstract | Links | BibTeX | Tags: artificial intelligence, Deep Learning, endoscopy, tumor detection

@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 = {artificial intelligence, Deep Learning, endoscopy, tumor detection},

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.

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.

Abstract | Links | BibTeX | Tags: Biological Effectiveness, Brachytherapy, Cellular Morphology, Microdosimetry, Patient-specific

@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 = {Biological Effectiveness, Brachytherapy, Cellular Morphology, Microdosimetry, Patient-specific},

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.

Abstract | Links | BibTeX | Tags: High dose rate brachytherapy, Interstitial brachytherapy, Intracavitary brachytherapy, Low dose rate brachytherapy, Number of brachytherapy treatments, Trends in utilization

@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 = {High dose rate brachytherapy, Interstitial brachytherapy, Intracavitary brachytherapy, Low dose rate brachytherapy, Number of brachytherapy treatments, Trends in utilization},

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.

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