Portrait of Shirin A. Enger

Shirin A. Enger

Associate Academic Member
shirin.enger@mila.quebec
Tenured Associate Professor, McGill University, Department of Oncology
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Biography

Shirin Abbasinejad Enger is a tenured associate professor in the Medical Physics Unit of the Gerald Bronfman Department of Oncology, McGill University.

She is also Director of the Medical Physics Unit, and a Tier 2 Canada Research Chair in Medical Physics.

Enger is also a principal investigator at the Lady Davis Institute for Medical Research and the Segal Cancer Centre of the Jewish General Hospital.

She received her PhD from Uppsala University in 2009 and was a postdoctoral fellow at Université Laval from 2009 to 2011. She has taken on a variety of leadership roles in international and national working groups and committees.

Current Students

Publications

Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization.
Hossein Jafarzadeh
Majd Antaki
Ximeng Mao
Marie Duclos
Farhad Maleki
Shirin A. Enger
OBJECTIVE Treatment plan optimization in high dose rate (HDR) brachytherapy often requires manual fine-tuning of penalty weights for each ob… (see more)jective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI). Approach: The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations. Main results: MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 seconds. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution. Significance: MOBO-qNEHVI can automatically explore the trade-offs between treatment plan objectives in a patient-specific manner within a minute. This approach can reduce the dependency of plan quality on planner's experience.
2024-04-26
Physics in Medicine and Biology (published)
doi.org
Radiation hardness of open Fabry-Pérot microcavities
Fernanda C. Rodrigues-Machado
Erika Janitz
Simon Bernard
H. Bekerat
Malcolm McEwen
James Renaud
Shirin A. Enger
Lilian Childress
Jack C Sankey
2024-04-24
Optics Express (published)
doi.org
arxiv.org
Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment.
Sébastien Quetin
Boris Bahoric
Farhad Maleki
Shirin A. Enger
OBJECTIVE Monte Carlo (MC) simulations are the benchmark for accurate radiotherapy dose calculations, notably in patient-specific high dose … (see more)rate brachytherapy (HDR BT), in cases where considering tissue heterogeneities is critical. However, the lengthy computational time limits the practical application of MC simulations. Prior research used Deep Learning (DL) for dose prediction as an alternative to MC simulations. While accurate dose predictions akin to MC were attained, GPU limitations constrained these predictions to large voxels of 3mm × 3mm × 3mm. This study aimed to enable dose predictions as accurate as MC simulations in 1mm × 1mm × 1mm voxels within a clinically acceptable timeframe. Approach: Computed tomography scans of 98 breast cancer patients treated with Iridium-192-based HDR BT were used: 70 for training, 14 for validation, and 14 for testing. A new cropping strategy based on the distance to the seed was devised to reduce the volume size, enabling efficient training of 3D DL models using 1 mm × 1 mm × 1 mm dose grids. Additionally, novel DL architecture with layer-level fusion were proposed to predict MC simulated dose to medium-in-medium (Dm,m). These architectures fuse information from TG-43 dose to water-in-water (Dw,w) with patient tissue composition at the layer-level. Different inputs describing patient body composition were investigated. Main results: The proposed approach demonstrated state-of-the-art performance, on par with the MC Dm,m maps, but 300 times faster. The mean absolute percent error for dosimetric indices between the MC and DL-predicted complete treatment plans was 0.17%±0.15% for the planning target volume V100, 0.30%±0.32% for the skin D2cc, 0.82%±0.79% for the lung D2cc, 0.34%±0.29% for the chest wall D2cc and 1.08%±0.98% for the heart D2cc. Significance: Unlike the time-consuming MC simulations, the proposed novel strategy efficiently converts TG-43 Dw,w maps into precise Dm,m maps at high resolution, enabling clinical integration.
2024-04-11
Physics in Medicine and Biology (published)
doi.org
Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy
Francisco Berumen
Samuel Ouellet
Shirin A. Enger
Luc Beaulieu
2024-03-14
Physics in Medicine and Biology (published)
doi.org
RapidBrachyTG43: A Geant4‐based TG‐43 parameter and dose calculation module for brachytherapy dosimetry
Jonathan Kalinowski
Shirin A. Enger
2024-01-22
Medical Physics (published)
doi.org
Investigation of the Dosimetry Characteristics of the GAFCHROMIC® EBT3 Film Response to Alpha Particle Irradiation
Mélodie Cyr
Victor D. Martinez
S. Devic
Nada Tomic
David F. Lewis
Shirin A. Enger
2023-11-01
Journal of Physics: Conference Series (published)
doi.org
Dosimetry of [18F]TRACK, the first PET tracer for imaging of TrkB/C receptors in humans
Alexander Thiel
Alexey Kostikov
Hailey Ahn
Youstina Daoud
Jean-Paul Soucy
Stephan Blinder
Carolin Jaworski
Carmen Wängler
Björn Wängler
Freimut Juengling
Shirin A. Enger
Ralf Schirrmacher
2023-10-23
EJNMMI Radiopharmacy and Chemistry (published)
doi.org
Transparent Anomaly Detection via Concept-based Explanations
Laya Rafiee Sevyeri
Ivaxi Sheth
Farhood Farahnak
Shirin A. Enger
2023-10-16
ArXiv (preprint)
doi.org
arxiv.org
AAPM Medical Physics Practice Guideline 14.a: Yttrium‐90 microsphere radioembolization
Nathan C. Busse
Muthana S. A. L. Al‐Ghazi
Nadine Abi‐Jaoudeh
Diane Alvarez
Ahmet S. Ayan
Erli Chen
Michael D. Chuong
William A. Dezarn
Shirin A. Enger
Stephen A. Graves
Robert F. Hobbs
Mary Ellen Jafari
S. Peter Kim
Nichole M. Maughan
Andrew M. Polemi
Jennifer R. Stickel
2023-10-11
Journal of Applied Clinical Medical Physics (published)
doi.org
M-TAG: A modular teaching-aid for Geant4
Liam Carroll
Shirin A. Enger
2023-09-19
Heliyon (published)
doi.org
122 Is Selenium-75 a Feasible HDR Brachytherapy Source?
Jake Reid
Shirin A. Enger
Jonathan Kalinowski
A. Armstrong
John Munro
2023-09-01
Radiotherapy and Oncology (published)
doi.org
124 Development of a Novel Dosimetry Software for Patient-Specific Intravascular Brachytherapy Treatment Planning on Optical Coherence Tomography Images
Maryam Rahbaran
Jonathan Kalinowski
James Man Git Tsui
Joseph DeCunha
Kevin Croce
Brian Bergmark
Philip Devlin
Shirin A. Enger
2023-09-01
Radiotherapy and Oncology (published)
doi.org