Preoperative prediction of the HER2 status and prognosis of patients with endometrial cancer using multiparametric MRI-based radiomics: a multicenter study

Non-invasive preoperative assessment of HER2 status is critical for identifying candidates for targeted therapy and personalizing treatment strategies in endometrial cancer (EC). This study aims to assess the preoperative value of multiparametric magnetic resonance imaging (MRI)-based radiomics in predicting HER2 status and prognosis of EC patients. We included 492 patients with EC divided into training (n = 215), internal validation (n = 92), and external validation cohorts 1 (n = 64) and 2 (n = 121). Models were constructed using six machine learning algorithms based on radiomics features derived from multiparametric MRI, including T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted sequences. A fusion model integrating key clinical predictors with the radiomics score (Rad-score) was created. Its predictive performance was evaluated through receiver operating characteristic (ROC) analysis, and its prognostic significance was assessed through survival analysis. HER2 (+) status was associated with poor differentiation and myometrial invasion in patients with EC. A support vector machine (SVM)-based model comprised of multiparametric MRI-based radiomics features demonstrated excellent performance in predicting HER2 status, with a mean area under the ROC curve (AUC) of 0.814 in the validation cohorts. A fusion model combining the SVM-based Rad-score with clinical factors significantly improved prediction accuracy, achieving AUCs of 0.914 in the training cohort, and 0.809-0.865 in the validation cohorts. Kaplan-Meier analysis revealed that patients with EC with predicted HER2 (+) status had worse progression-free survival than those with predicted HER2 (-) status. The fusion model based on multiparametric MRI-based radiomics features can potentially aid in the accurate preoperative prediction of HER2 status and prognosis of patients with EC, providing essential insights for clinical decision-making.