Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review.

ABSTRACT:

Background

This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. Key prognostic endpoints, including overall survival (OS), recurrence-free survival (RFS), progression-free survival (PFS), and treatment response prediction (TRP), are examined to evaluate the effectiveness of these algorithms and identify significant features that influence predictive accuracy.

Recent findings

A thorough search of four major databases-PubMed, Scopus, Web of Science, and Cochrane-resulted in 2400 articles published within the last decade, with 32 studies meeting the inclusion criteria. Notably, most publications emerged after 2021. Commonly used algorithms for survival prediction included random forest,

SUBMITTER: Asadi F 

PROVIDER: S-EPMC11920737 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature