Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma

Abstract

Abstract Purpose To evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC). Materials and methods A retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test. Results In predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807–0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1). Conclusions The combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making.

Keywords

• Endometrial adenocarcinoma
• Magnetic resonance imaging
• Radiomics
• Machine learning
• Lymphovasular space invasion