Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.

ABSTRACT:

Objectives

The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.

Methods

Information of EOC patients from Samsung Medical Center (training cohort, n=1,128) was analyzed to optimize the prognostic model using GB. The performance of the final model was externally validated with patient information from Asan Medical Center (validation cohort, n=229). The area under the curve (AUC) by the GB model was compared to that of the conventional Cox proportional hazard regression analysis (CoxPHR) model.

Results

In the training cohort, the AUC of the GB model for predicting second year overall surviv

SUBMITTER: Paik ES 

PROVIDER: S-EPMC6543110 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature