Interpretable prognostic modeling of endometrial cancer
Endometrial carcinoma (EC) is one of the most common gynecological cancers in the world. In this work we apply Cox proportional hazards (CPH) and optimal survival tree (OST) algorithms to the retrospective prognostic modeling of disease-specific survival in 842 EC patients. We demonstrate that the linear CPH models are preferred for the EC risk assessment based on clinical features alone, while the interpretable, non-linear OST models are favored when patient profiles are enriched with tumor molecular data. By studying the OST decision path structure, we show how explainable tree models recapitulate existing clinical knowledge prioritizing L1 cell-adhesion molecule and estrogen receptor status indicators as key risk factors in the p53 abnormal EC subgroup. We believe that visually interpretable tree algorithms are a promising method to explore feature interactions and generate novel research hypotheses. To aid further clinical adoption of advanced machine learning techniques, we stress the importance of quantifying model discrimination and calibration performance in the development of explainable clinical prediction models.