John Kildea

2026-01-22

Physics in Medicine & Biology (publié)

Reference radiation selection is confirmed as a significant source of relative biological effectiveness variation for neutrons.

Laura C Paterson

Stephen Pecoskie

Farrah Norton

Norma Ybarra

Richard B Richardson

2025-12-08

International Journal of Radiation Biology (publié)

Development of a defacing algorithm to protect the privacy of head and neck cancer patients in publicly-accessible radiotherapy datasets

Kayla O'Sullivan‐Steben

Luc Galarneau

2025-11-25

Medical Physics (publié)

In silico Neutron Relative Biological Effectiveness Estimations For Pre-DNA Repair And Post-DNA Repair Endpoints

Nicolas Desjardins

2025-10-07

bioRxiv (prépublication)

Development of a defacing algorithm to protect the privacy of head and neck cancer patients in publicly-accessible radiotherapy datasets

Kayla O'Sullivan‐Steben

Luc Galarneau

2025-08-19

ArXiv (prépublication)

Quantification of head and neck cancer patients'anatomical changes during radiotherapy: prediction of replanning need

Odette Rios-Ibacache

James Manalad

Kayla O'Sullivan‐Steben

Emily Poon

Luc Galarneau

Julia Khriguian

Georges Shenouda

2025-07-18

ArXiv (prépublication)

Quantification of head and neck cancer patients' anatomical changes during radiotherapy: Toward the prediction of replanning need

Odette Rios‐Ibacache

James Manalad

Kayla O'Sullivan‐Steben

Emily Poon

Luc Galarneau

Julia Khriguian

George Shenouda

Abstract Background Head and neck cancer (HNC) patients undergoing radiotherapy (RT) may experience anatomical changes during treatment, whi… (voir plus)ch can compromise the validity of the initial treatment plan, necessitating replanning. However, ad hoc replanning disrupts clinical workflows and increases workload. Currently, no standardized method exists to quantify anatomical variation that necessitates replanning. Purpose This project aimed to create geometrical metrics to describe anatomical changes in HNC patients during RT. The usefulness of these metrics was evaluated by a univariate analysis and through machine learning (ML) models to predict the need for replanning. Methods A cohort of 150 HNC patients treated at McGill University Health Centre was analyzed. Based on the shapes of the RT structures (body, PTV, mandible, neck, and submandibular contours), we developed 43 metrics and automatically calculated them through a Python pipeline that we called HNGeoNatomyX. Univariate analysis using linear regression was conducted to obtain the rate of change of each metric. We also obtained the relative variation of each metric between the pre‐treatment and replanning‐requested scans. Fraction‐specific ML models (incorporating information available up to and including the specific fraction) for fractions 5, 10, and 15 were built using metrics, clinical data, and feature selection techniques. Model performance was estimated with repeated stratified 5‐fold cross‐validation resampling technique and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results Univariate analysis showed that body‐ and neck‐related metrics were most predictive of replanning need. Our best specific multivariate models for fractions 5, 10, and 15 yielded testing scores of 0.82, 0.70, and 0.79, respectively. Our models early predicted replanning for 76% of the true positives. Conclusions The created metrics have the potential to characterize and distinguish which patients will necessitate RT replanning. They show promise in guiding clinicians to evaluate RT replanning for HNC patients and streamline workflows.

2025-07-17

ArXiv (prépublication)

Relative biological effectiveness of 31 meV thermal neutrons in peripheral blood lymphocytes

Laura C Paterson

Fawaz Ali

Mohsen Naseri

David Perez Loureiro

Amy Festarini

Marilyne Stuart

Chad Boyer

Ronald Rogge

Christie Costello

Norma Ybarra

Richard B Richardson

2025-03-10

Radiation Protection Dosimetry (publié)

Who is your ideal peer mentor? A qualitative study to identify cancer patient preferences for a digital peer support app

Loes Knaapen

Andrea M. Laizner

Kelly Agnew

Xiao Jian Du

Douaa El Abiad

Luc Galarneau

Susie Judd

James Manalad

Ridhi Mittal

Tristan Williams

Brandon Woolfson

Angele Wen

2025-03-06

Supportive Care in Cancer (publié)

RadiSeq: a single- and bulk-cell whole-genome DNA sequencing simulator for radiation-damaged cell models

Felix Mathew

Luc Galarneau

Objective To build and validate a simulation framework to perform single-cell and bulk-cell whole genome sequencing simulation of radiation-… (voir plus)exposed Monte Carlo cell models to assist radiation genomics studies. Approach Sequencing the genomes of radiation-damaged cells can provide useful insight into radiation action for radiobiology research. However, carrying out post-irradiation sequencing experiments can often be challenging, expensive, and time-consuming. Although computational simulations have the potential to provide solutions to these experimental challenges, and aid in designing optimal experiments, the absence of tools currently limits such application. Monte Carlo toolkits exist to simulate radiation exposures of cell models but there are no tools to simulate single- and bulk-cell sequencing of cell models containing radiation-damaged DNA. Therefore, we aimed to develop a Monte Carlo simulation framework to address this gap by designing a tool capable of simulating sequencing processes for radiation-damaged cells. Main Results We developed RadiSeq – a multi-threaded whole-genome DNA sequencing simulator written in C++. RadiSeq can be used to simulate Illumina sequencing of radiation-damaged cell models produced by Monte Carlo simulations. RadiSeq has been validated through comparative analysis, where simulated data were matched against experimentally obtained data, demonstrating reasonable agreement between the two. Additionally, it comes with numerous features designed to closely resemble actual whole-genome sequencing. RadiSeq is also highly customizable with a single input parameter file. Significance RadiSeq enables the research community to perform complex simulations of radiation-exposed DNA sequencing, supporting the optimization, planning, and validation of costly and time-intensive radiation biology experiments. This framework provides a powerful tool for advancing radiation genomics research.

2025-02-09

bioRxiv (prépublication)

Patient Engagement in the Implementation of Electronic Patient-Reported Outcome Tools: The Experience of Two Early-Adopter Institutions in the Pan-Canadian Radiotherapy Patient-Reported Outcome Initiative

Amanda Caissie

J. Lane

B. Barber

S. Chisholm

2024-12-01

Clinics in oncology (publié)

Patient Engagement in the Implementation of Electronic Patient Reported Outcome (ePRO) Tools: The Experience of Two Early Adopter Institutions in the pan-Canadian Radiotherapy PRO Initiative

Amanda Caissie

Jennifer Lane

Brittany V Barber

Sue Chisholm

2024-12-01

Clinics in oncology (publié)