Multiparametric MRI-based radiomics and deep learning for differentiating uterine serous carcinoma from endometrioid carcinoma: a multicenter retrospective study.

ABSTRACT:

Background

Uterine serous carcinoma (USC) and endometrioid endometrial carcinoma (EEC) are distinct subtypes of endometrial cancer with markedly different prognoses and management strategies. Accurate preoperative differentiation between USC and EEC is of great significance for tailoring surgical planning and adjuvant therapy.

Purpose

To develop and validate a multiparametric MRI-based radiomics and deep learning (DL) model for preoperative distinguishing USC from EEC.

Methods

A total of 210 patients (68 USCs and 142 EECs) from four hospitals who underwent preoperative MRI were enrolled in this retrospective study. Features from radiomics and deep learning were extracted using T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast enhanced MRI (CE-

SUBMITTER: Shen Y 

PROVIDER: S-EPMC12540183 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature