Validation of machine learning-based models to predict and explain the risk of ovarian cancer: a multicentric study on <i>BRCA</i>-mutated patients undergoing risk-reducing salpingo-oophorectomy.

ABSTRACT:

Objective

BRCA-mutated women are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing, due to the lack of effective methods that could be able to early detect the occurrence of ovarian cancer. Thus, predictive machine learning (ML) techniques could be crucial to aid clinicians in identifying high-risk BRCA-mutated patients and determining the appropriate timing for performing RRSO.

Methods

In this work, we addressed this task by developing explainable ML models using clinical data referred to a multicentric cohort of 694 BRCA-mutated patients from six Italian centers (Policlinico Gemelli, IRCCS San Gerardo, Policlinico Bari, Istituto Tumori Regina Elena, Istituto Tumori Giovanni Paolo II, Ospedale F. Miulli), who

SUBMITTER: Loizzi V 

PROVIDER: S-EPMC12037974 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature