UroFusion-X: a unified multimodal deep learning framework for robust diagnosis, subtyping, and prognosis of urological cancers

Multimodal clinical data, including imaging, pathology, omics, and laboratory tests, are often fragmented in routine practice, leading to inconsistent decision-making in the management of urological cancers. We propose UroFusion-X, a unified multimodal framework for integrated diagnosis, molecular subtyping, and prognosis prediction of bladder, kidney, and prostate cancers, with inherent robustness to missing modalities. The system incorporates 3D imaging encoders, pathology multiple-instance learning, omics graph networks, and a TabTransformer for laboratory and clinical variables. A cross-modal co-attention mechanism combined with a gated product-of-experts fusion strategy enables effective representation alignment across heterogeneous inputs, while anatomy-pathology consistency constraints and patient-level contrastive learning further enhance interpretability and generalization. Prognostic modeling is achieved via DeepSurv and DeepHit survival heads. Evaluated on a multi-center real-world cohort with external validation and leave-one-center-out testing, UroFusion-X consistently outperformed strong unimodal and simple fusion baselines, maintained over 90% of its predictive performance under substantial modality dropout, and demonstrated higher net clinical benefit in decision curve analysis. These results indicate that the proposed framework can improve decision consistency and reduce unnecessary testing when deployed in real clinical workflows.