A foundation model with weak experiential guidance in detecting muscle invasive bladder cancer on MRI.

Preoperative detection of muscle-invasive bladder cancer (MIBC) remains a great challenge in practice. We aimed to develop and validate a deep Vesical Imaging Network (ViNet) model for the detection of MIBC using high-resolution Tweighted MR imaging (hrTWI) in a multicenter cohort. ViNet was designed using a modified 3D ResNet, in which, the encoder layers were pretrained using a self-supervised foundation model on over 40,000 cross-modal imaging datasets for transfer learning, and the classification modules were weakly supervised by an experiential knowledge-domain mask indicated by a nnUNet segmentation model. Optimal ViNet model was trained in derivation data (cohort 1, n = 312) and validated in multicenter data (cohort 2, n = 79; cohort 3, n = 44; cohort 4, n = 56) across a multi-ablation-test for model selection. In internal validation, ViNet using hrTWI outperformed all ablation-test models (odds ratio [OR], 7.41 versus 1.85 - 2.70; all P < 0.05). In external validation, the performance of ViNet using hrTWI versus ablation-test models was heterogeneous (OR, 1.31 - 3.89 versus 0.89 - 9.75; P = 0.03 - 0.15). In addition, clinical benefit of ViNet was evaluated between six readers using the Vesical Imaging-Reporting and Data System (VI-RADS) versus ViNet-adjusted VI-RADS. As a result, ViNet-adjusted VI-RADS upgraded 62.9% (17/27) of MIBC missed in VI-RADS score 2, while downgraded 84.1% (69/84), 62.5% (35/56) and 67.9% (19/28) of non-muscle-invasive bladder cancer (NMIBC) overestimated in VI-RADS score 3-5. We concluded that ViNet presents a promising alternative for diagnosing MIBC using hrT2WI instead of conventional multiparametric MRI.