Artificial Intelligence

@article{nokey,

title = {Beyond single-run metrics with CP-fuse: A rigorous multi-cohort evaluation of clinico-pathological fusion for improved survival prediction in TCGA},

author = {Juan Duran and Yujing Zou and Martin Vallières and Shirin A. Enger},

url = {https://www.sciencedirect.com/science/article/pii/S2666827025001720},

doi = {https://doi.org/10.1016/j.mlwa.2025.100789},

issn = {2666-8270},

year  = {2025},

date = {2025-12-01},

journal = {Machine Learning with Applications},

volume = {22},

number = {100789},

abstract = {Accurate prediction of progression-free survival (PFS) is critical for precision oncology. However, most existing multimodal survival studies rely on single fusion strategies, one-off cross-validation runs, and focus solely on discrimination metrics, leaving gaps in systematic evaluation and calibration. We evaluated multimodal fusion approaches combining histopathology whole-slide images (via Hierarchical Image Pyramid Transformer) and clinical variables (via Feature Tokenizer-Transformer) across five TCGA cohorts: bladder cancer (BLCA), uterine corpus endometrial carcinoma (UCEC), lung adenocarcinoma (LUAD), breast cancer (BRCA), and head and neck squamous cell carcinoma (HNSC) (N=2,984). Three intermediate (marginal, cross-attention, Variational Autoencoder or VAE) and two late fusion strategies (trainable-weight, meta-learning) were trained end-to-end with DeepSurv. Our 100-repetition 10-fold cross-validation (CV) framework mitigates the variance overlooked in single-run CV evaluations. VAE fusion achieved superior PFS prediction (Concordance-index) in BLCA (0.739±0.019), UCEC (0.770±0.021), LUAD (0.683±0.018), and BRCA (0.760±0.021), while meta-learning was best for HNSC (0.686±0.022). However, Integrated Brier Score values (0.066–0.142) revealed calibration variability. Our findings highlight the importance of multimodal fusion, combined discrimination and calibration metrics, and rigorous validation for clinically meaningful survival modeling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Automatic catheter digitization in breast brachytherapy},

author = {Sébastien Quetin and Hossein Jafarzadeh and Jonathan Kalinowski and Hamed Bekerat and Boris Bahoric and Farhad Maleki and Shirin A. Enger},

url = {https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.18107},

doi = {https://doi.org/10.1002/mp.18107},

issn = {2473-4209},

year  = {2025},

date = {2025-09-12},

urldate = {2025-09-12},

journal = {Medical Physics},

volume = {52},

number = {e18107},

issue = {9},

abstract = {Background:

High dose rate (HDR) brachytherapy requires clinicians to digitize catheters manually. This process is time-consuming, complex, and depends heavily on clinical experience-especially in breast cancer cases, where catheters may be inserted at varying angles and orientations due to an irregular anatomy.



Purpose:

This study is the first to automate catheter digitization specifically for breast HDR brachytherapy, emphasizing the unique challenges associated with this treatment site. It also introduces a pipeline that automatically digitizes catheters, generates dwell positions, and calculates the delivered dose for new breast cancer patients.



Methods:

Treatment data from 117 breast cancer patients treated with HDR brachytherapy were used. Pseudo-contours for the catheters were created from the treatment digitization points and divided into three classes: catheter body, catheter head, and catheter tip. An nnU-Net pipeline was trained to segment the pseudo-contours on treatment planning computed tomography images of 88 patients (training and validation). Then, pseudo-contours were digitized by separating the catheters into connected components. Predicted catheters with an unusual volume were flagged for manual review. A custom algorithm was designed to report and separate connected components containing colliding catheters. Finally, a spline was fitted to every separated catheter, and the tip was identified on the spline using the tip contour prediction. Dwell positions were placed from the created tip at a regular step size extracted from the DICOM plan file. Distance from each dwell position used during the clinical treatment to the fitted spline (shaft distance) was computed, as well as the distance from the treatment tip to the one identified by our pipeline. Dwell times from the clinical plan were assigned to the nearest generated dwell positions. TG-43 dose in water was computed analytically, and the absorbed dose in the medium was predicted using a published AI-based dose prediction model. Dosimetric comparison between the clinically delivered plan dose and the created automated plan dose was evaluated regarding dosimetric indices percent error.



Results:

Our pipeline was used to digitize 408 catheters on a test set of 29 patients. Shaft distance was on average 0.70 ± 3.91 mm and distance to the tip was on average 1.37 ± 5.25 mm. The dosimetric error between the manual and automated treatment plans was, on average, below 3% for planning target volume V100, V150, V200 and for the lung, heart, skin, and chest wall D2cc and D1cc, in both water and heterogeneous media. For D0.1cc values in all the organs at risk, the average error remained below 5%. The pipeline execution time, including auto-contouring, digitization, and dose to medium prediction, averages 118 s, ranging from 63 to 294 s. The pipeline successfully flagged all cases where digitization was not performed correctly.



Conclusions:

Our pipeline is the first to automate the digitization of catheters for breast brachytherapy, as well as the first to generate dwell positions and predict corresponding AI-based absorbed dose to medium based on automatically digitized catheters. The automatically digitized catheters are in excellent agreement with the manually digitized ones while more accurately reflecting their true anatomical shape.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Tumour nuclear size heterogeneity as a biomarker for post-radiotherapy outcomes in gynecological malignancies},

author = {Yujing Zou and Harry Glickman and Manuela Pelmus and Farhad Maleki and Boris Bahoric and Magali Lecavalier-Barsoum and Shirin A. Enger},

url = {https://www.phiro.science/article/S2405-6316(25)00098-3/fulltext},

doi = {10.1016/j.phro.2025.100793},

issn = {2405-6316},

year  = {2025},

date = {2025-07-01},

journal = {Physics and Imaging in Radiation Oncology},

volume = {35},

number = {100793},

abstract = {Background and purpose: Radiotherapy targets DNA in cancer cell nuclei. Radiation dose, however, is prescribed to a macroscopic target volume assuming uniform distribution, failing to consider microscopic variations in dose absorbed by individual nuclei. This study investigated a potential link between pre-treatment tumour nuclear size distributions and post-radiotherapy outcomes in gynecological squamous cell carcinoma (SCC).



Materials and methods: Our multi-institutional cohort consisted of 191 non-metastatic gynecological SCC patients who had received radiotherapy with diagnostic whole slide images (WSIs) available. Tumour nuclear size distribution mean and standard deviation were extracted from WSIs using deep learning, and used to predict progression-free interval (PFI) and overall survival (OS) in multivariate Cox proportional hazards (CoxPH) analysis adjusted for age and clinical stage.



Results: Multivariate CoxPH analysis revealed that a larger nuclear size distribution mean results in more favorable outcomes for PFI (HR = 0.45, 95% CI: 0.19 - 1.09, p = 0.084) and OS (HR = 0.55, 95% CI: 0.24 - 1.25, p = 0.16), and that a larger nuclear size standard deviation results in less favorable outcomes for PFI (HR = 7.52, 95% CI: 1.43 - 39.52, p = 0.023) and OS (HR = 4.67, 95% CI: 0.96 - 22.57, p = 0.063). The bootstrap-validated C-statistic was 0.56 for PFI and 0.57 for OS.



Conclusion: Despite low accuracy, tumour nuclear size heterogeneity aided prognostication over standard clinical variables and was associated with outcomes following radiotherapy in gynecological SCC. This highlights the potential importance of personalized multiscale dosimetry and warrants further large-scale pan-cancer studies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer},

author = {Björn Morén and Hossein Jafarzadeh and Shirin A Enger},

url = {https://www.sciencedirect.com/science/article/pii/S0010482525003713?via%3Dihub},

doi = {https://doi.org/10.1016/j.compbiomed.2025.110020},

issn = {1879-0534},

year  = {2025},

date = {2025-05-01},

journal = {Computers in Biology and Medicine},

volume = {190},

number = {110020},

abstract = {Background: High dose rate brachytherapy (HDR BT) is a common treatment modality for cancer. In HDR BT, a radioactive source is placed inside or close to a tumor, aiming to give a high enough dose to the tumor, while sparing nearby healthy tissue and organs at risk. Treatment planning of HDR BT for prostate cancer consists of two types of decisions, placement of catheters, modeled with binary variables, and dwell times, modeled with continuous non-negative variables. Optimal spatial placement of catheters is important for avoiding local recurrence and complications, but such characteristics have not been modeled for the combined treatment planning problem of catheter placement and dwell time optimization.



Method: We propose a data-driven approach using linear regression, mutual information, and random forests to find convex estimates of spatial dose characteristics that correlate well with contiguous volumes receiving a too-high (hot spots) or too-low dose (cold spots). These estimates were incorporated in retrospective treatment plan optimization of 28 prostate cancer patients.



Results: The proposed hot-spot terms reduced the volume receiving twice the prescribed dose by 29% at 14 catheters. Also, the results illustrate the trade-offs between the number of catheters and spatial dose characteristics.



Conclusions: Our study demonstrates that incorporating a term for hot spots in the objective function of the treatment planning model is more effective in reducing hot spots than catheter placements that are not optimized for hot spots.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Trade-off of different deep learning-based auto-segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI},

author = {Alana Thibodeau-Antonacci and Marija Popovic and Ozgur Ates and Chia-Ho Hua and James Schneider and Sonia Skamene and Carolyn Freeman and Shirin Abbasinejad Enger and James Man Git Tsui},

url = {https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.17782},

doi = {10.1002/mp.17782},

issn = {2473-4209},

year  = {2025},

date = {2025-04-01},

journal = {Medical Physics},

volume = {52},

issue = {6},

pages = {3541–3556},

abstract = {Background: As auto-segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. One notable example is the use of adult-trained commercial software for the contouring of organs at risk (OARs) of pediatric patients.



Purpose: This study aimed to compare three auto-segmentation approaches in the context of pediatric craniospinal irradiation (CSI): commercial, out-of-the-box, and in-house.



Methods: CT scans from 142 pediatric patients undergoing CSI were obtained from St. Jude Children's Research Hospital (training: 115; validation: 27). A test dataset comprising 16 CT scans was collected from the McGill University Health Centre. All images underwent manual delineation of 18 OARs. LimbusAI v1.7 served as the commercial product, while nnU-Net was trained for benchmarking. Additionally, a two-step in-house approach was pursued where smaller 3D CT scans containing the OAR of interest were first recovered and then used as input to train organ-specific models. Three variants of the U-Net architecture were explored: a basic U-Net, an attention U-Net, and a 2.5D U-Net. The dice similarity coefficient (DSC) assessed segmentation accuracy, and the DSC trend with age was investigated (Mann-Kendall test). A radiation oncologist determined the clinical acceptability of all contours using a five-point Likert scale.



Results: Differences in the contours between the validation and test datasets reflected the distinct institutional standards. The lungs and left kidney displayed an increasing age-related trend of the DSC values with LimbusAI on the validation and test datasets. LimbusAI contours of the esophagus were often truncated distally and mistaken for the trachea for younger patients, resulting in a DSC score of less than 0.5 on both datasets. Additionally, the kidneys frequently exhibited false negatives, leading to mean DSC values that were up to 0.11 lower on the validation set and 0.07 on the test set compared to the other models. Overall, nnU-Net achieved good performance for body organs but exhibited difficulty differentiating the laterality of head structures, resulting in a large variation of DSC values with the standard deviation reaching 0.35 for the lenses. All in-house models generally had similar DSC values when compared against each other and nnU-Net. Inference time on the test data was between 47-55 min on a Central Processing Unit (CPU) for the in-house models, while it was 1h 21m with a V100 Graphics Processing Unit (GPU) for nnU-Net.



Conclusions: LimbusAI could not adapt well to pediatric anatomy for the esophagus and the kidneys. When commercial products do not suit the study population, the nnU-Net is a viable option but requires adjustments. In resource-constrained settings, the in-house model provides an alternative. Implementing an automated segmentation tool requires careful monitoring and quality assurance regardless of the approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Dosimetric impact of positional uncertainties and a robust optimization approach for rectal intensity-modulated brachytherapy},

author = {Björn Morén and Alana Thibodeau-Antonacci and Jonathan Kalinowski and Shirin A. Enger},

url = {https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.17800},

doi = {10.1002/mp.17800},

issn = {0094-2405},

year  = {2025},

date = {2025-03-31},

journal = {Medical Physics},

volume = {52},

issue = {6},

pages = {3528–3540},

abstract = {Background: Intensity-modulated brachytherapy (IMBT) employs rotating high-Z shields during treatment to decrease radiation in certain directions and conform the dose distribution to the target volume. Prototypes for dynamic IMBT have been proposed for prostate, cervical, and rectal cancer.



Purpose: We considered two shielded applicators for IMBT rectal cancer treatment and investigated how rotational uncertainties in the shield angle and translational uncertainties in the source position affect plan evaluation criteria.



Methods: The effect of rotational errors of 3∘ , 5∘ and 10∘ , and translational errors of 1, 2 and 3 mm on evaluation criteria were investigated for shields with 

180

∘

 and 

90

∘

 emission windows. Further, a robust optimization approach based on quadratic penalties that includes scenarios with errors was proposed. The extent to which dosimetric effects of positional errors can be mitigated with this model was evaluated compared to a quadratic penalty model without scenarios with errors. A retrospective rectal cancer data set of ten patients was included in this study. Treatment planning was performed using the Monte Carlo-based treatment planning system, RapidBrachyMCTPS.



Results: For the largest investigated rotational error of 

±

10

∘

 , the clinical target volume 

D

90

 remained, on average, within 

5

%

 of the result without error, while the contralateral healthy rectal wall experienced an increase in the mean 

D

0.1

c

c

 , 

D

2

c

c

 , and 

D

50

 of 

26

%

 , 

9

%

 , and 

1

%

 for the 

180

∘

 shield and of 32%, 9%, and 2% for the 

90

∘

 shield. For translational errors of 

±

2

 mm, there were increases in dosimetric indices for both the superior (sup) and inferior (inf) dose spill regions. Specifically, for the 

180

∘

 shield, the 

D

0.1

c

c

 , 

D

2

c

c

 , and 

D

50

 increased by 

13

%

 , 

11

%

 , and 

10

%

 , respectively, for the sup region, and by 

26

%

 , 

15

%

 , and 

11

%

 , respectively, for the inf region. Similar results were obtained with the 

90

∘

 shield. Overall, the robust and traditional models had similar results. However, the number of active dwell positions obtained with the robust model was larger, and the longest dwell time was shorter.



Conclusions: We have quantified the effect of rotational shield and translational source errors of various magnitudes on evaluation criteria for rectal IMBT. The robust optimization approach was generally not able to mitigate positional errors. However, it resulted in more homogeneous dwell times, which can be beneficial in conventional high-dose-rate brachytherapy to avoid hot spots around specific dwell positions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization},

author = {Hossein Jafarzadeh and Majd Antaki and Ximeng Mao and Marie Duclos and Farhard Maleki and Shirin A Enger },

doi = {10.1088/1361-6560/ad4448},

year  = {2024},

date = {2024-05-21},

urldate = {2024-05-21},

journal = {Physics in Medicine & Biology},

volume = {69},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {TransMedTech Excellence Scholarship (Doctoral Award)},

author = {Yujing Zou },

url = {https://transmedtech.org/en/training/transmedtech-institute-excellence-scholarships/},

year  = {2022},

date = {2022-09-01},

urldate = {2022-09-01},

organization = {The Institut TransMedTech},

key = {award},

type = {award},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zou2022brachyestro,

title = {ESTRO 2022: innovations in brachytherapy},

author = {Yujing Zou and David-Santiago Ayala Alvarez },

url = {https://www.estro.org/About/Newsroom/Newsletter/Brachytheraphy/ESTRO-2022-innovations-in-brachytherapy-Brachyther},

year  = {2022},

date = {2022-06-29},

urldate = {2022-06-29},

journal = {ESTRO Newsletter},

pages = {754--767},

publisher = {The European SocieTy for Radiotherapy and Oncology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{weishaupt2022approaching,

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 Richard Choo and Bradley J Stish and Shirin A Enger and Christopher Deufel},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Brachytherapy},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{deufel_deep_2021,

title = {Deep learning for automated applicator reconstruction in high-dose-rate prostate brachytherapy},

author = {Christopher Deufel and Luca L. Weishaupt and Hisham Kamal Sayed and Chunhee Choo and Bradley Stish},

url = {https://www.estro.org/Congresses/WCB-2021/811/poster-physics/3229/deeplearningforautomatedapplicatorreconstructionin},

year  = {2021},

date = {2021-01-01},

journal = {World Congress of Brachytherapy 2021},

note = {Type: Journal Article},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mao_rapidbrachydl_2020,

title = {RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning},

author = {Ximeng Mao and Joelle Pineau and Roy Keyes and Shirin A. Enger},

doi = {10.1016/j.ijrobp.2020.04.045},

issn = {1879-355X},

year  = {2020},

date = {2020-11-01},

journal = {International Journal of Radiation Oncology, Biology, Physics},

volume = {108},

number = {3},

pages = {802--812},

abstract = {PURPOSE: Detailed and accurate absorbed dose calculations from radiation interactions with the human body can be obtained with the Monte Carlo (MC) method. However, the MC method can be slow for use in the time-sensitive clinical workflow. The aim of this study was to provide a solution to the accuracy-time trade-off for 192Ir-based high-dose-rate brachytherapy by using deep learning. 

METHODS AND MATERIALS: RapidBrachyDL, a 3-dimensional deep convolutional neural network (CNN) model, is proposed to predict dose distributions calculated with the MC method given a patient's computed tomography images, contours of clinical target volume (CTV) and organs at risk, and treatment plan. Sixty-one patients with prostate cancer and 10 patients with cervical cancer were included in this study, with data from 47 patients with prostate cancer being used to train the model. 

RESULTS: Compared with ground truth MC simulations, the predicted dose distributions by RapidBrachyDL showed a consistent shape in the dose-volume histograms (DVHs); comparable DVH dosimetric indices including 0.73% difference for prostate CTV D90, 1.1% for rectum D2cc, 1.45% for urethra D0.1cc, and 1.05% for bladder D2cc; and substantially smaller prediction time, acceleration by a factor of 300. RapidBrachyDL also demonstrated good generalization to cervical data with 1.73%, 2.46%, 1.68%, and 1.74% difference for CTV D90, rectum D2cc, sigmoid D2cc, and bladder D2cc, respectively, which was unseen during the training. 

CONCLUSION: Deep CNN-based dose estimation is a promising method for patient-specific brachytherapy dosimetry. Desired radiation quantities can be obtained with accuracies arbitrarily close to those of the source MC algorithm, but with much faster computation times. The idea behind deep CNN-based dose estimation can be safely extended to other radiation sources and tumor sites by following a similar training process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}