Multiparameter MRI-based radiomics analysis for preoperative prediction of type II endometrial cancer.

ABSTRACT:

Objectives

This study aimed to develop and validate a radiomics nomogram based on multiparameter MRI for preoperative differentiation of type II and type I endometrial carcinoma (EC).

Methods

A total of 403 EC patients from two centers were retrospectively recruited (training cohort, 70 %; validation cohort, 30 %). Radiomics features were extracted from T2-weighted imaging, dynamic contrast-enhanced T1-weighted imaging at delayed phase(DCE4), and apparent diffusion coefficient (ADC) maps. Following dimensionality reduction, radiomics models were developed by logistic regression (LR), random forest (RF), bootstrap aggregating (Bagging), support vector machine (SVM), artificial neural network (ANN), and naive bayes (NB) algorithms. The diagnostic performance of each radiomics

SUBMITTER: Cao Y 

PROVIDER: S-EPMC11234004 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature