Portrait de Shirin A. Enger

Shirin A. Enger

Membre académique associé
Professeure agrégée, McGill University, Département d'oncologie
Sujets de recherche
Apprentissage automatique médical
Apprentissage profond
Biologie computationnelle
Site web
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Biographie

Shirin Abbasinejad Enger est professeure agrégée à l'Unité de physique médicale du Département d'oncologie Gerald Bronfman de l’Université McGill, directrice de l'Unité de physique médicale et titulaire d'une chaire de recherche du Canada de niveau 2 en physique médicale. Elle est également chercheuse principale à l'Institut Lady Davis de recherches médicales et au Centre de cancérologie Segal de l'Hôpital général juif. Mme Enger a obtenu un doctorat de l'Université d'Uppsala en 2009 et a été boursière postdoctorale à l'Université Laval de 2009 à 2011. Elle a joué un rôle de premier plan au sein de plusieurs groupes de travail et comités nationaux et internationaux.

Étudiants actuels

Joel Beaudry
Doctorat - McGill
Juan Duran
Doctorat - McGill
Farhood Farahnak
Postdoctorat - McGill
Harry Glickman
Doctorat - McGill
Hossein Jafarzadeh
Doctorat - McGill
Maryam Maryam
Doctorat - McGill
Site web
Github
Sébastien Quetin
Doctorat - McGill
Laya Rafiee Sevyeri
Postdoctorat - McGill
Google Scholar
Alana Thibodeau-Antonacci
Doctorat - McGill
Yujing Zou
Doctorat - McGill
Site web
Github
Google Scholar

Publications

Trade‐off of different deep learning‐based auto‐segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI
Alana Thibodeau‐Antonacci
Marija Popovic
Ozgur Ates
Chia‐Ho Hua
James Schneider
Sonia Skamene
Carolyn Freeman
Shirin A. Enger
James Man Git Tsui
As auto‐segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. … (voir plus)One notable example is the use of adult‐trained commercial software for the contouring of organs at risk (OARs) of pediatric patients.
2025-04-01
Medical Physics (Lancaster) (publié)
doi.org
A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer.
Björn Morén
Hossein Jafarzadeh
Shirin A. Enger
2025-03-26
Comput. Biol. Medicine (publié)
doi.org
Isolating the impact of tissue heterogeneities in high dose rate brachytherapy treatment of the breast
Jules Faucher
Vincent Turgeon
Boris Bahoric
Shirin A. Enger
Peter G.F. Watson
2025-02-01
Physics and Imaging in Radiation Oncology (publié)
doi.org
Development of small, cost‐efficient scintillating fiber detectors for automated synthesis of positron emission tomography radiopharmaceuticals
Hailey Ahn
Liam Carroll
Robert Hopewell
I-Huang Tsai
Dean Jolly
Gassan Massarweh
Shirin A. Enger
2024-09-20
Medical Physics (Lancaster) (publié)
doi.org
Relative biological effectiveness of clinically relevant photon energies for the survival of human colorectal, cervical, and prostate cancer cell lines
Joanna Li
N. Chabaytah
Joud Babik
Behnaz Behmand
H. Bekerat
Tanner Connell
Michael D C Evans
Russell Ruo
T. Vuong
Shirin A. Enger
2024-09-19
Physics in Medicine & Biology (publié)
doi.org
169Yb-based high dose rate intensity modulated brachytherapy for focal treatment of prostate cancer
Maude Robitaille
Cynthia Ménard
Gabriel Famulari
Dominic Béliveau-Nadeau
Shirin A. Enger
2024-07-01
Brachytherapy (publié)
doi.org
Pioneering women in nuclear and radiation sciences
Mirta Dumancic
Shirin A. Enger
2024-06-01
Radiotherapy and Oncology (publié)
doi.org
Investigation of dosimetric characteristics of radiochromic film in response to alpha particles emitted from Americium-241.
Victor D. Diaz‐Martinez
Mélodie Cyr
S. Devic
Nada Tomic
David F. Lewis
Shirin A. Enger
BACKGROUND In radiotherapy, it is essential to deliver prescribed doses to tumors while minimizing damage to surrounding healthy tissue. Acc… (voir plus)urate measurements of absorbed dose are required for this purpose. Gafchromic® external beam therapy (EBT) radiochromic films have been widely used in radiotherapy. While the dosimetric characteristics of the EBT3 model film have been extensively studied for photon and charged particle beams (protons, electrons, and carbon ions), little research has been done on α
2024-05-20
Medical Physics (Lancaster) (publié)
doi.org
Automatic segmentation of Organs at Risk in Head and Neck cancer patients from CT and MRI scans
Sébastien Quetin
Andrew Heschl
Mauricio Murillo
Rohit Murali
Shirin A. Enger
Farhad Maleki
Background and purpose: Deep Learning (DL) has been widely explored for Organs at Risk (OARs) segmentation; however, most studies have focus… (voir plus)ed on a single modality, either CT or MRI, not both simultaneously. This study presents a high-performing DL pipeline for segmentation of 30 OARs from MRI and CT scans of Head and Neck (H&N) cancer patients. Materials and methods: Paired CT and MRI-T1 images from 42 H&N cancer patients alongside annotation for 30 OARs from the H&N OAR CT&MR segmentation challenge dataset were used to develop a segmentation pipeline. After cropping irrelevant regions, rigid followed by non-rigid registration of CT and MRI volumes was performed. Two versions of the CT volume, representing soft tissues and bone anatomy, were stacked with the MRI volume and used as input to an nnU-Net pipeline. Modality Dropout was used during the training to force the model to learn from the different modalities. Segmentation masks were predicted with the trained model for an independent set of 14 new patients. The mean Dice Score (DS) and Hausdorff Distance (HD) were calculated for each OAR across these patients to evaluate the pipeline. Results: This resulted in an overall mean DS and HD of 0.777 +- 0.118 and 3.455 +- 1.679, respectively, establishing the state-of-the-art (SOTA) for this challenge at the time of submission. Conclusion: The proposed pipeline achieved the best DS and HD among all participants of the H&N OAR CT and MR segmentation challenge and sets a new SOTA for automated segmentation of H&N OARs.
2024-05-17
ArXiv (prépublication)
doi.org
arxiv.org
Characterizing the voxel-based approaches in radioembolization dosimetry with reDoseMC.
Taehyung Peter Kim
Shirin A. Enger
BACKGROUND Yttrium-90 ( 90 Y …
2024-05-04
Medical Physics (publié)
doi.org
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… (voir plus)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 (publié)
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 (publié)
doi.org
arxiv.org