Cross-Modal Deep Learning for Integrated Molecular Diagnosis of Cancer: A Prospective Multicenter Study

Accurate integration of histological and molecular features is central to modern cancer diagnostics, but it is often hampered by resource-intensive parallel workflows, limited tissues, and increased diagnostic complexity. We present CAMPaS (Cross-modal AI for Integrated Molecular Pathology Diagnosis and Stratification), a clinical AI prototype that addresses challenges in real-world translation of jointly predicting glioma histology, molecular markers, and WHO 2021 integrative diagnoses from hematoxylin and eosin-stained slides. Trained and validated on 3,367 patients (6,043 slides) across eight cohorts (six retrospective, two prospective), CAMPaS achieved high diagnostic performance (AUC 0.895-0.916 in training; 0.946-0.955 in prospective cohorts) and generalized robustly across diverse settings. Its interpretable cross-modal predictions aligned with histopathological annotations and genomic profiles, revealing biologically coherent features. CAMPaS identified histological features for molecular markers, and its clinical utility was validated for enhancing real-world clinical diagnostics. Crucially, CAMPaS stratifies prognosis and treatment response, offering a scalable and biologically grounded solution to accelerate precision oncology.