Join the Machine Learning in Medical Imaging Consortium (MaLMIC) for an opportunity to network on machine learning
Monthly Virtual Forum Series on Zoom
Friday, January 22, 2021
3:30 to 5:30 p.m. EST
Dr. Lang, a radiation oncologist, discussed the unmet clinical need followed by two researchers, Dr. Mattonen and Mr. Kazmierski, described their machine learning research work in the area. This was followed by discussion focused on opportunities to collaborate and share resources.
Monthly Virtual Forum Series on Zoom
Friday, January 22, 2021
3:30 to 5:30 p.m. EST
Dr. Lang, a radiation oncologist, discussed the unmet clinical need followed by two researchers, Dr. Mattonen and Mr. Kazmierski, described their machine learning research work in the area. This was followed by discussion focused on opportunities to collaborate and share resources.
Predictive modelling in head and neck cancer – a clinician’s wish list
Pencilla Lang, BEng. MD. FRCPC. PhD
Radiation Oncologist, London Health Sciences Centre
Assistant Professor, Department of Oncology, Western University
Pencilla reviewed some of the clinical challenges faced by oncologists treating head and neck cancer, and outlined some opportunities for predictive models, radiomics and AI to guide clinical decision-making.
Radiomics and machine learning for oropharyngeal cancer
Sarah Mattonen, PhD
Assistant Professor, Department of Medical Biophysics, Western University
Sarah discussed preliminary work investigating radiomics and machine learning models to predict outcomes and toxicities in patients with oropharyngeal cancer treated with chemoradiotherapy. Sarah also discussed efforts to validate existing models, and described some challenges and opportunities for radiomics research in head and neck cancer.
Machine learning for prognostic modelling in head and neck cancer using multimodal data: lessons from the RADCURE Prognostic Modelling Challenge
Michal Kazmierski, BSc
MSc Student, Department of Medical Biophysics, University of Toronto
Michal discussed the results of a machine learning challenge for head and neck cancer (HNC) survival prediction with the aim of 1) developing an accurate prognostic model for HNC survival using clinical, demographic and routinely collected CT imaging data and 2) evaluating the true added value of CT radiomics compared to other prognostic factors.