A prognostic system for epithelial ovarian carcinomas using machine learning
A prognostic system for epithelial ovarian carcinomas using machine learning
P. Grimley,Zhenqiu Liu,5 Authors,Dechang Chen
TLDR
EACCD was successfully applied to integrate A and H with T, N, and M for stratification and survival prediction of epithelial ovarian carcinoma patients, and could be advantageously incorporated to test the prognostic impact of emerging diagnostic or therapeutic advances.
Abstract
Integrating additional factors into the International Federation of Gynecology and Obstetrics (FIGO) staging system is needed for accurate patient classification and survival prediction. In this study, we tested machine learning as a novel tool for incorporating additional prognostic parameters into the conventional FIGO staging system for stratifying patients with epithelial ovarian carcinomas and evaluating their survival.