Multimodal MRI Radiomics Features and Deep Learning Features in Predicting Lymphovascular Space Invasion in Endometrial Carcinoma

Background Lymphovascular space invasion (LVSI) is a key prognostic indicator in endometrial cancer, impacting disease progression, treatment strategies and overall survival of patients. The accurate identifying of LVSI of endometrial cancer before surgery is challenging due to certain limitations in radiological methods. Objective To develop an efficient approach for predicting LVSI in endometrial cancer before surgery using multimodal MRI Radiomics and deep learning, offering a crucial foundation for clinical treatment. Methods Two radiologists, unaware of pathology, manually outlined the region of interest (ROI) on preoperative DWI, T1WI + C, and T2WI images. The intersection of their ROIs was the final one. radiomics features were extracted from the ROI, and depth features from the largest - area ROI slice in MRI images. In the training set, 8 models were built via logistic regression after feature reduction. These models were tested against pathology, and the area under the receiver operating characteristic curve (AUC) was calculated. The best - performing imaging model was combined with a deep - learning model to form a hybrid, and its performance was evaluated.​ Results 308 patients were split 7:3 into a training set (n = 215) and a test set (n = 93). The LR model performed best in LVSI prediction. Combining clinical and radiomics with the LR algorithm enhanced performance. Deep - learning mixed - feature models had AUCs of 0.948 (training) and 0.704 (test), while mixed models had 0.953 (training) and 0.720 (test).​ Conclusion Deep - learning mixed - feature and mixed models are most effective for preoperative LVSI prediction in endometrial cancer, guiding clinical decision - making.