Using machine learning to create a precision prognostication system for endometrial cancer

Abstract

Objective: A variety of risk stratification systems have been developed to predict prognosis and tailor adjuvant therapy for endometrial cancer. These systems rely on a variety of variables including stage, histology, and age but often classify patients with widely variable risks into similar categories. We used a novel machine learning algorithm to develop a precision prognostic system for high-intermediate and high-risk endometrial cancer.