Machine learning models using multiparametric MRI for preoperative risk stratification in endometrial cancer.

ABSTRACT: This study evaluated the efficacy of machine learning and radiomics of preoperative multiparameter MRIs in predicting low- vs high-risk histopathologic features and early vs advanced FIGO stage (IA vs IB or higher) in endometrial cancer. This retrospective study of patients with endometrial cancer histologically confirmed from 2008 through 2023 excluded those with: (a) previous treatment for endometrial carcinoma, (b) incomplete MRI examinations or low-quality MR images, (c) incomplete pathology reports, (d) non-visualized tumors on MRI, or (e) distant metastases. In total, 110 radiomic features were extracted using commercial PACS built-in software following segmentation after sagittal T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI), and diffusion weighted imag

SUBMITTER: Vy VPT 

PROVIDER: S-EPMC11626267 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature