Abstract:

In radiation therapy treatment planning optimization, selecting a set of clinical objectives that are tractable and parsimonious yet clinically effective is a challenging task. In clinical practice, this is typically done by trial and error based on the treatment planner's subjective assessment, which often makes the planning process inefficient and inconsistent. We develop the objective selection problem that infers a sparse set of objectives for prostate cancer treatment planning based on historical treatment data. We formulate the problem as a non-convex bilevel mixed-integer program using inverse optimization and highlight its connection with feature selection to propose greedy heuristics as well as application-specific methods that utilize anatomical information of the  patients. Our results show that the proposed heuristics find objectives that are near optimal. Using curve analysis for dose-volume histograms, we show that the learned objectives closely represent latent clinical preferences by recovering historical treatment for each patient.

Bio:

Tayo is a fifth-year PhD candidate at Rice University in the Department of Computational and Applied Mathematics. Tayo's research interests include integer programming theory and healthcare applications, particularly in cancer treatment. He is a Visiting Graduate Student at The University of Texas MD Anderson Cancer Center in the Department of Radiation Oncology.