MRI-based deep learning combined with radiomics for the preoperative prediction of lymphovascular invasion in patients with bladder cancer

Lymphovascular invasion (LVI) signifies poor prognosis in bladder cancer, yet reliable preoperative prediction remains challenging, limiting personalized treatment planning. In this multi-center retrospective study, 543 bladder cancer patients were enrolled. Of these, 473 patients from two centers were randomly split into training and internal test sets at an 8:2 ratio, while 70 patients from six additional centers constituted an independent external test set. After tumor and peritumoral segmentation, a hybrid model that integrates deep learning and radiomics features was developed. The hybrid model was compared against ten baseline models, including image-only and radiomics-only deep learning models, as well as radiomics-based machine learning approaches. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) with five-fold cross-validation. Prognostic value was evaluated by Kaplan–Meier survival analysis with the log-rank test. The hybrid model achieved AUCs of 0.77 (internal test) and 0.75 (external test), surpassing all baseline models. The model-predicted LVI status significantly stratified overall survival in the training set ( p < 0.001) and the internal test set ( p = 0.003), with a non-significant trend observed in the external test set ( p = 0.151). The proposed non-invasive hybrid model can accurately predict LVI status preoperatively and demonstrates prognostic potential, thereby aiding risk stratification in bladder cancer.