Prediction of muscular-invasive bladder cancer using multi-view fusion self-distillation model based on 3D...

Accurate preoperative differentiation between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is crucial for surgical decision-making in bladder cancer (BCa) patients. MIBC diagnosis relies on the Vesical Imaging-Reporting and Data System (VI-RADS) in clinical using multi-parametric MRI (mp-MRI). Given the absence of some sequences in practice, this study aims to optimize the existing T2-weighted imaging (T2WI) sequence to assess MIBC accurately.

We analyzed T2WI images from 615 BCa patients and developed a multi-view fusion self-distillation (MVSD) model that integrates transverse and sagittal views to classify MIBC and NMIBC. This 3D image classification method leverages z-axis information from 3D MRI volume, combining information from adjacent slices for comprehensive features extraction. Multi-view fusion enhances global information by mutually complementing and constraining information from the transverse and sagittal planes. Self-distillation allows shallow classifiers to learn valuable knowledge from deep layers, boosting feature extraction capability of the backbone and achieving better classification performance.

Compared to the performance of MVSD with classical deep learning methods and the state-of-the-art MRI-based BCa classification approaches, the proposed MVSD model achieves the highest area under the curve (AUC) 0.927 and accuracy (Acc) 0.880, respectively. DeLong's test shows that the AUC of the MVSD has statistically significant differences with the VGG16, Densenet, ResNet50, and 3D residual network. Furthermore, the Acc of the MVSD model is higher than that of the two urologists.

Our proposed MVSD model performs satisfactorily distinguishing between MIBC and NMIBC, indicating significant potential in facilitating preoperative BCa diagnosis for urologists.