Demonstrating the power and potential of dose-surface maps to investigate spatial effects of treatment planning parameters on delivered dose to the rectum

This paper presents a COMP, demonstrating the power and potential of dose-surface maps to investigate spatial effects of treatment planning parameters on delivered dose to the rectum.

Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest

This paper presents a novel methodology to distinguish between metastatic and healthy bone lesions using lesion-center-based geometric regions of interest, radiomics, and machine learning.

Striving to Fill in Gaps between Clinical Practice and Standards-The Evolution of a Pan-Canadian Approach to Patient-Reported Outcomes Use

This paper describes the pan-Canadian efforts of the Canadian Partnership for Quality Radiotherapy to champion the use of patient-reported outcomes in Canadian radiation oncology practice.

High-Accuracy Relative Biological Effectiveness Values Following Low-Dose Thermal Neutron Exposures Support Bimodal Quality Factor Response with Neutron Energy

This paper examines the relative biological effectiveness of DNA damage generated by thermal neutrons compared to gamma radiation. It uses experimental data gathered using the 64 meV neutron beam at the National Research Universal reactor at Canadian Nuclear Labs.

Developing an mHealth Application to Coordinate Nurse-Provided Respite Care Services for Families Coping With Palliative-Stage Cancer - Protocol for a User-Centered Design Study

This paper describes a protocol to develop a mobile health app prototype for coordinating respite care services for families coping with palliative-stage cancer in Quebec.

Natural language processing and machine learning to assist radiation oncology incident learning

This paper, we describe an effort that began as an undergraduate term-research project of Hui Wang to semi-automate the process of categorizing incident reports in radiation oncology using natural language processing.

Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports - Generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases

This paper, which describes a method to extract physician-reported pain from clinical notes, is the outcome of the first part of the PhD project of student Hossein Naseri. Hossein's is developing an NLP and radiomics pipeline to predict pain in patients with bone metastases in order to potentially enable prophylactic palliative radiotherapy treatments.

Towards the characterization of neutron carcinogenesis through direct action simulations of clustered DNA damage

This paper, which is the culmination of Logan Montgomery's PhD studies, describes Logan's work simulating the direct action of ionizing radiation on a geometrical model of human DNA using the TOPAS-nBio framework. It builds upon our previous NICE research, in particular the MSc project of Chris Lund.

Satisfaction among Cancer Patients Undergoing Radiotherapy during the COVID-19 Pandemic: An Institutional Experience

The COVID-19 pandemic has shifted oncology practices to prioritize patient safety while maintaining necessary treatment delivery. We obtained patient feedback on pandemic-based practices in our radiotherapy department to improve quality of patient …

Acceptability of a Patient Portal (Opal) in HIV Clinical Care: A Feasibility Study

Opal (opalmedapps.com), a patient portal in use at the Cedars Cancer Centre of the McGill University Health Centre (MUHC) (Montreal, Canada), gives cancer patients access to their medical records, collects information on patient-reported outcome …