Machine learning-based radiomics model: prognostic prediction and mechanism exploration in patients with endometrial cancer.

ABSTRACT:

Objectives

To investigate the predictive value of machine-learning-based Radiomics models for postoperative overall survival (OS) of endometrial cancer (EC) patients and their biological mechanisms.

Methods

Data from 469 patients with endometrial cancer in three Centers (271 in Center 1, 154 in Center 2, and 44 in Center 3) were retrospectively and 90 patients in Center 1 were prospectively analyzed. Three-dimensional Radiomics parameters of the primary lesion and its surrounding 5 mm region in T2WI were collected from all patients. Ten machine learning methods were used to calculate the optimal Radiomics score (Radscore), whose incremental value to the available clinical indexes, pathomics, transcriptomics, and proteomics were revealed. Eventually, TCGA and CPTAC were used

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12481825 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature