At a Glance

-Machine learning-based radiation treatment planning
-Automated quality assurance for radiation oncology using machine learning methods
-Radiomics and patient outcomes prediction
-Complexity and quality assessment for radiation oncology
-Explainable artificial intelligence for radiation oncology workflow

Short Bio

Dr. Thomas Purdie graduated from McMaster University, Hamilton, Ontario, Canada with a B.Sc. (Hons.) in the Medical and Health Physics program of the Department of Physics and Astronomy in 1997. He then completed his Ph.D. in the Department of Medical Biophysics at the University of Western Ontario in London, Ontario, Canada in 2002. Following graduate school, he completed a medical physics residency and research fellowship at Princess Margaret Cancer Centre (Toronto, Ontario Canada) in 2005. He is currently a Staff Medical Physicist in the Radiation Medicine Program, Princess Margaret Cancer Centre and became a Board Certified Medical Physicist (CCPM – Radiation Therapy Physics) in 2007. He is an Associate Professor in the Departments of Medical Biophysics and Radiation Oncology, University of Toronto and a Clinician Scientist, Princess Margaret Research Institute, University Health Network. Dr. Purdie’s research focuses on developing and clinically implementing machine learning based treatment planning and quality assurance (QA) processes in radiation oncology.