Dr. Sangjune Laurence Lee is a Clinical Assistant Professor within the Department of Oncology, Division of Radiation Oncology. He completed an undergraduate degree in electrical engineering at the University of Waterloo, a master’s degree in biomedical engineering at Johns Hopkins University, and a medical degree at the University of Toronto. He continued his residency training in Toronto and did a fellowship at the University of Wisconsin to gain experience in real-time MRI-guided radiotherapy. He specializes in the management of gastrointestinal and breast malignancies.
Lee, Sangjune Laurence
Radiation Oncology
Clinical Assistant Professor
Biography
Area of Focus
- esophageal and gastroesophageal junction cancers
- organ-preservation strategies
- MR-guided radiotherapy
- artificial intelligence
- opportunistic diagnoses
Summary of Research
Dr. Lee’s research advances personalized radiation therapy through the integration of clinical trials, magnetic resonance imaging, and artificial intelligence. A major focus is improving outcomes for patients with esophageal and gastroesophageal junction cancers, including evaluating organ-preserving treatment strategies and leading the NACEA phase II trial of hypofractionated neoadjuvant chemoradiotherapy. His work in MR-guided radiotherapy explores online adaptation, respiratory gating, quantitative imaging, and automated organ contouring to improve treatment precision and reduce toxicity. Supported by CIHR and institutional grants, his emerging breast cancer research uses radiotherapy planning CT scans and deep learning to identify osteoporosis and predict future fracture risk. He also studies AI education, clinical training, and quality improvement to support the safe and effective adoption of new technologies in radiation oncology
Area Of Focus
- esophageal and gastroesophageal junction cancers
- organ-preservation strategies
- MR-guided radiotherapy
- artificial intelligence
- opportunistic diagnoses
Summary Of Research
Dr. Lee’s research advances personalized radiation therapy through the integration of clinical trials, magnetic resonance imaging, and artificial intelligence. A major focus is improving outcomes for patients with esophageal and gastroesophageal junction cancers, including evaluating organ-preserving treatment strategies and leading the NACEA phase II trial of hypofractionated neoadjuvant chemoradiotherapy. His work in MR-guided radiotherapy explores online adaptation, respiratory gating, quantitative imaging, and automated organ contouring to improve treatment precision and reduce toxicity. Supported by CIHR and institutional grants, his emerging breast cancer research uses radiotherapy planning CT scans and deep learning to identify osteoporosis and predict future fracture risk. He also studies AI education, clinical training, and quality improvement to support the safe and effective adoption of new technologies in radiation oncology