Preoperative prediction of aggressive endometrial cancer using multiparametric MRI-based deep transfer learning models.

ABSTRACT:

Background

Accurate preoperative prediction of endometrial cancer (EC) aggressiveness is critical for individualized treatment planning. This study proposes a method for integrating multimodal data to improve the prediction of aggressiveness in EC.

Methods

A total of 207 patients with pathologically confirmed EC were retrospectively enrolled. The patients were randomized (7:3) into a training cohort (n=144) and a test cohort (n=63). All patients underwent preoperative MRI including T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient mapping, and contrast-enhanced T1-weighted imaging (CE-T1WI). Deep learning (DL) models using ResNet50, ResNet101, DenseNet121 were employed to extract deep transfer learning (DTL) features. Three decision-level fusion

SUBMITTER: Guo R 

PROVIDER: S-EPMC12668923 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature