To develop a unified framework, UroFusion-X, for the integrated diagnosis, molecular subtyping, and prognosis prediction of urological cancers, specifically addressing challenges such as data heterogeneity, missing modalities, and the need for robust clinical applicability.
Key Findings:
UroFusion-X outperforms unimodal and simple fusion baselines in diagnostic, subtyping, and prognostic tasks, indicating its superior capability in clinical decision-making.
The framework retains ≥90% performance under modality dropout, demonstrating robustness to incomplete data and reliability in real-world applications.
Higher net clinical benefit observed in decision curve analysis, suggesting practical utility and improved patient outcomes.
Interpretation:
UroFusion-X's integrated approach enhances decision-making consistency and reduces unnecessary testing in urological cancer management, with significant potential for broad clinical application and improved patient care.
Limitations:
The framework's performance may still be influenced by the quality and diversity of the training datasets, particularly in underrepresented populations.
Real-world clinical implementation may face challenges related to data integration and standardization across different institutions, such as varying data formats and collection protocols.
Conclusion:
UroFusion-X represents a significant advancement in the multimodal analysis of urological cancers, offering a robust solution for integrating diverse data types while addressing key challenges in clinical settings.
