Radiomics-Based Machine Learning for the Detection of Myometrial Invasion in Endometrial Cancer: Systematic Review and Meta-Analysis.

ABSTRACT:

Background

Preoperative endometrial cancer (EC) diagnosis often depends on radiologists' expertise, which introduces subjectivity. Recent studies have explored radiomics-based machine learning (ML) models for detecting myometrial invasion (MI), but a comprehensive evaluation of their diagnostic performance is lacking. Therefore, our study systematically assessed the diagnostic performance of radiomics-based ML approaches for identifying MI in EC, thereby providing evidence to guide the development or improvement of noninvasive diagnostic tools.

Objective

This study aims to systematically assess the diagnostic performance of radiomics-based ML approaches for identifying MI in EC and compare the diagnostic efficacy of conventional ML (CML) and deep learning (DL) models based o

SUBMITTER: Jiang W 

PROVIDER: S-EPMC12699251 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature