Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer

ABSTRACT:

Background

Tumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC.

Methods

One hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features a

SUBMITTER: Yue X 

PROVIDER: S-EPMC9989162 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature