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 imaging (DWI). The radiomic features from each imaging sequence were utilized for initial modeling. A combined model, which included features retained from all 3 sequences, was then established. The area under the receiver operating characteristic curve (AUC) determined the efficacy of each model. For 5 specific histopathologic features, the combined model achieved AUCs of 0.87 (95% CI, 0.85-0.90), 0.90 (95% CI, 0.88-0.92), 0.88 (95% CI, 0.87-0.90), 0.88 (95% CI, 0.86-0.92), and 0.87 (95% CI, 0.86-0.90). This model incorporated 38 radiomic features: 12 from T2WI, 17 from CE-T1WI, and 9 from DWI. In conclusion, an MRI radiomics-based model can differentiate between early- and advanced-stage endometrial cancer and between low- and high-risk histologic markers, giving it the potential to predict high risk and stratify preoperative risk in those with endometrial cancer. The findings may aid personalized preoperative assessments to guide clinical decision-making in endometrial cancer.