Using machine learning approaches to predict tumour recurrence and progression to provide personalized and compassionate care for non-muscle invasive bladder cancer patients
Award: 2023 Compassion and Artificial Intelligence Grant
- AI Model
- Bladder Cancer
- Machine Learning
Surgical Oncologist, Division of Urology at Trillium Health Partners
Bladder cancer is the most expensive cancer to treat due to high recurrence rates and a need for lifelong disease surveillance. Current monitoring strategies are costly, patient unfriendly, and lack supporting evidence. Existing predictive models perform poorly and do not capture changes in patients’ cancer course after initial diagnosis. Andrew’s study aims to integrate complete cancer timelines of each patient with machine learning and compassionate care approaches to improve prediction for tumour recurrence and progression.