Machine Learning for Preoperative Assessment and Postoperative Prediction in Cervical Cancer: Multicenter Retrospective Model Integrating MRI and Clinicopathological Data.

ABSTRACT:

Background

Machine learning (ML) has been increasingly applied to cervical cancer (CC) research. However, few studies have combined both clinical parameters and imaging data. At the same time, there remains an urgent need for more robust and accurate preoperative assessment of parametrial invasion and lymph node metastasis, as well as postoperative prognosis prediction.

Objective

The objective of this study is to develop an integrated ML model combining clinicopathological variables and magnetic resonance image features for (1) preoperative parametrial invasion and lymph node metastasis detection and (2) postoperative recurrence and survival prediction.

Methods

Retrospective data from 250 patients with CC (2014-2022; 2 tertiary hospitals) were analyzed. Variables were

SUBMITTER: Li S 

PROVIDER: S-EPMC12431160 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature