Our Artificial Intelligence Group aims to move towards personalized healthcare for cancer patients, applied to different medical modalities such as diagnostic and histopathology images, textual reports, genomics data, etc. using deep learning methods. Our AI group is in active partnership and collaboration with the Mila – Quebec AI Institute (the world’s largest academic research center for deep learning), TransMedTech, Radiomics, Exactis, MyPL, and the AC Camargo Cancer Center, Sao Paulo, Brazil. Founded in 2021, we are also home to an eight-week summer school McMedHacks on deep learning applications in medical image analysis which attracted 356 participants from 38 countries and 1100 participants from 62 countries.
Robust Optimization in HDR brachytherapy
Robust optimization for IMBT rectal cancer Intensity modulated brachytherapy (IMBT) is a delivery technique for brachytherapy (BT) in which shields are used to further modulate the dose distribution, as compared to conventional high dose-rate (HDR) BT. For rectal cancer, our group has designed and built a shielded applicator. In this project, we investigate rotational uncertainties of the applicator and what effect those could have on evaluation criteria such as the dose-volume histogram. Furthermore, a robust optimization approach is proposed to mitigate these effects from uncertainty. Catheter placement for prostate HDR brachytherapy – Investigating trade-offs between spatial properties and number of catheters We study the treatment planning project of HDR BT for prostate cancer including both decisions on catheter placements and dwell times. Our focus is to improve spatial properties such as contiguous volumes that receive a high or a low dose, referred to as hot spots and cold spots, respectively. Further, we investigate the trade-offs between the number of inserted catheters and the sizes of hot spots and cold spots.
AI-based treatment planning for brachytherapy applications
Treatment plan optimization is a routine part of both external beam radiotherapy and high dose rate brachytherapy. High dose rate brachytherapy is a mode of internal radiotherapy in which a source of radiation is placed inside the tumor through hollow needles known as catheters (interstitial brachytherapy) or near the tumor through site specific applicators (intracavitary brachytherapy). For every patient, the process of treatment plan optimization ensures that the dose to the tumor region is sufficient while the dose to the surrounding organs at risk is minimal. Therefore, treatment plan optimization is a key process in ensuring positive radiotherapy outcome.
In interstitial high dose rate brachytherapy, treatment planning is a labor-intensive and time-consuming task. The treatment planning cannot begin before the patient is put under anesthesia and the catheters are inserted. This makes the process painful for the patient and costly for the hospital. In addition, the current optimization method only controls the amount of time for which the source dwells at a specific location inside a catheter. The number and the location of the catheters are not optimized.
Hossein investigates the use of reinforcement learning for optimizing the number and position of the catheters prior to catheter insertion, in addition to the dwell time optimization. This way, quality of the treatment will improve because the catheters will be inserted according to an optimized plan.
Deep Learning-Based Multimodality Treatment Outcome Prediction
Cancer is the leading cause of death in Canada. Radiotherapy is used in about half of all cancer treatments. However, it is clinically challenging to predict which patients will benefit from which treatment combination despite improved protocols, imaging techniques for cancer management, and combining various radiotherapy treatment modalities. An accurate method for predicting a patient’s likelihood of response may reduce unnecessary interventions, lower healthcare costs, and reduce side effects. Therefore, investigating how pre-treatment patient characteristics influence treatment efficacy as measured by post-treatment response is crucial. The aim of Yujing and Alana’s work is to develop a patient-specific, machine learning-based multi-modal treatment outcome prediction model for patients suffering from gynecological cancers. Our group has recently shown that the patient-specific radiation dose response may be influenced by inter-patient variation in tumor nuclei size. Therefore, the models developed by Yujing and Alana will not only integrate diagnostic images such as Computed Tomography, Magnetic Resonance, and Ultrasound images to detect high-order features that determine treatment response on a patient-specific basis, but also tumor nucleus size and cell spacing data obtained from scanned digital images of histopathology slides. Correlation between cell morphology and treatment outcome will also be investigated
Inter-observer variability of the manual segmentation
Alana is investigating the inter-observer variability of the manual segmentation of tumor regions in endoscopy images and its effect on treatment outcomes. Furthermore, they are developing a deep learning-based segmentation tool that can learn from multiple observers labels with high inter-observer variability.
AI-based dosimetry and catheter reconstruction for brachytherapy application
The precalculated dose used by clinical treatment planning systems is based on the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43) formalism which describes dose deposition around a single source centrally positioned in a spherical water phantom with unit density. The influence of patient tissue and applicator heterogeneities, intersource attenuation, and finite patient dimensions are ignored. Recently, more advanced model based dose calculation algorithms (MBDCAs) calculating dose to medium in medium have been developed. In MBDCAs, dose calculations are performed using the patient’s CT or MR images. Voxel-by-voxel assignment of tissue mass density and elemental composition is required. Tissue mass density is obtained from CT or synthetic CT images using a Hounsfield Unit (HU) to density calibration curve. Tissue composition and nominal mass density can also be assigned to contoured organs. Exact description of the source and applicator geometry, material composition, and nominal mass density is required as well. MBDCAs such as the Monte Carlo method, provide a detailed and accurate method for calculation of absorbed dose in heterogeneous systems such as the human body however are too time consuming to be used in a clinical workflow.
The main goal of Sébastien’s PhD project is to develop a precise and automated dosimetry algorithm that will take into account patient’s tissue/applicator heterogeneities and replace the time consuming Monte-Carlo simulations. Sébastien is also working on automating patient organ/tumor segmentation as well as catheter/applicator reconstruction to decrease the total treatment time.
Artificial intelligence with radiomics and deep learning for the diagnosis of malignancy in pancreatic cysts: a pilot study
Pancreatic cysts are common, with a prevalence of 15% in the general population, and up to 37% in older populations. While some cystic lesions of the pancreas, such as serous cystadenomas, pseudocysts and epidermoid cysts, have little to no malignant potential, others including intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm carry a risk of invasive adenocarcinoma. Even among the potentially malignant cyst types, it is difficult to predict which cysts harbor malignancy or have a high risk of malignant transformation. In this project, we aim to 1) develop a deep learning algorithm using radiomics from EUS, CT and MR images of pancreas cysts to predict malignancy; 2) To assess the diagnostic ability of this prediction algorithm in differentiating malignant from benign pancreatic cysts; 3) To compare the diagnostic ability of this AI algorithm to standard of care criteria for predicting malignant pancreatic cystic neoplasms; 4) To assess feasibility of the current methodology and AI algorithm for a large multicenterd trial with an external validation cohort.
Auto-segmentation of brain structures
Radiation therapy to brain tumor requires a delicate balance between delivering adequate tumorcidal dose to the tumor lesion while minimizing radiation to healthy organs. Segmentation of organs at risk (OARs) is time-consuming and subject to inter-observer variability. Auto-segmentation can 1) increase workflow output by lightening segmentation time, and 2) decrease inter-observer variability. The Radiation Oncology Department at the McGill University Health Centre in collaboration with the Montreal Children Hospital treat pediatric patients with brain tumors. Compared to adult population, radiation therapy to pediatric population is higher stake for several reasons.1) As opposed to adult patients with mature anatomy, radiation can several impair normal growth of pediatric patients; 2) There are more brain structures in pediatric patients to consider; 3) Most children brain structures are more sensitive to radiation than those of adults. Thus, accurate segmentation is of great importance. The project is to investigate deep learning algorithms that can accurately auto-segment OARs in pediatric patients undergoing brain radiation therapy.
McMedHacks is an 8-week program about deep learning and medical image analysis and accumulates in a hackathon, where participants can solve real-life clinical medical image analysis challenges. The program consists of weekly presentations and workshops that are led by students as well as leaders in medical image analysis from academia and industry. Our mission is to bridge domains and bring deep learning in to the clinic by making it accessible to anyone.
Artificial Intelligence-based Brachytherapy Presentation
From New avenues in the non-operative management of patients with rectal cancer Conference, 14.10.2022.
Deep Learning Framework : Tensorboard and Pytorch Lightning Workshop
ESTRO 2022: innovations in brachytherapy Journal Article
In: ESTRO Newsletter, pp. 754–767, 2022.
Deep Learning Framework : Pytorch tensors and Autograd Workshop
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).
Fonds de recherche du Québec - Santé (FRQS) PhD doctoral training scholarship award
2022, ($84,000 for 2022 - 2026).
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.).
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.).
Monte-Carlo Based Simulations of the Uncertainties in Clinical Water-Based Intravascular Brachytherapy Dosimetry Presentation
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.
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.
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.
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.
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.
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.
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.
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.
T I Gurman Prize in Physics award
Canada Graduate Scholarship – Doctoral Program award
Deep learning based tumor segmentation of endoscopy images for rectal cancer patients Presentation
ESTRO Annual meeting, 27.08.2021.
Mitacs Globalink Research Award award
Inter-Observer Variability and Deep Learning in Rectal Tumor Segmentation from Endoscopy Images Presentation
The COMP Annual Scientific Meeting 2021, 22.06.2021.
RapidBrachyMCTPS: An open-source dose calculation and optimization tool for brachytherapy research Presentation
Deep learning-based tumor segmentation on digital images of histopathology slides for microdosimetry applications Journal Article
In: arXiv:2105.01824 [physics], 2021, (arXiv: 2105.01824).
OC-0112 development of a dynamic-shielding intensity modulated endorectal brachytherapy applicator Presentation
Radiotherapy and Oncology, 01.05.2021, ISBN: 0167-8140, 1879-0887.
Treatment outcome Prediction for gynecological cancers patients with a machine learning model using pre/post diagnostic image modalities and digital histopathology images Presentation
CRUK RadNet Manchester AI for Optimising Radiotherapy Outcomes Workshop, 10.02.2021.
Fire-Up - Radiation Treatment Outcome Prediction Presentation
Fire-Up Presentation, 09.02.2021.
Approaching automated applicator digitization from a new angle: using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy Journal Article
In: 2021 ABS Annual Meeting, 2021, (Type: Journal Article).
Deep learning for automated applicator reconstruction in high-dose-rate prostate brachytherapy Journal Article
In: World Congress of Brachytherapy 2021, 2021, (Type: Journal Article).
RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning Journal Article
In: International Journal of Radiation Oncology, Biology, Physics, vol. 108, no. 3, pp. 802–812, 2020, ISSN: 1879-355X.
Graduate Excellence Fellowship award
Approaching automated applicator digitization from a new angle - Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy Presentation
12.06.2020, (Type: Journal Article).
Math And Physics Class Of 1965 Prize award
RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning Journal Article
In: Int J Radiat Oncol Biol Phys, vol. 108, no. 3, pp. 802-812, 2020, ISSN: 0360-3016.
Classification and Segmentation of Tumor Cells and Nuclei On Biopsy Slides Using Deep Learning for Microdosimetry Applications Journal Article
In: 2020 Joint AAPM textbar COMP Virtual Meeting, 2020, (Type: Journal Article).