A prognostic system for epithelial ovarian carcinomas using machine learning.

ABSTRACT:

Introduction

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.

Material and methods

Cancer-specific survival data for epithelial ovarian carcinomas were extracted from the Surveillance, Epidemiology, and End Results (SEER) program. Two datasets were constructed based upon the year of diagnosis. Dataset 1 (39 514 cases) was limited to primary tumor (T), regional lymph nodes (N) and distant metastasis (M)

SUBMITTER: Grimley PM 

PROVIDER: S-EPMC8360140 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature