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 extended turnaround times and resource-intensive parallel workflows, which can delay diagnosis and introduce variability between pathologists. We present CAMPaS (Cross-modal AI for Integrated Molecular Pathology Diagnosis and Stratification), a trustworthy AI framework that jointly predicts glioma histology, molecular markers, and WHO 2021 integrative diagnoses from hematoxylin and eosin-stained slides. CAMPaS introduces a novel architecture purposefully designed to address challenges in real-world translation. Trained on 3,367 patients (6,043 slides) across eight cohorts (six retrospective, two prospective), CAMPaS achieved high diagnostic accuracy (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 expert annotations and genomic profiles, revealing biologically coherent features. Crucially, CAMPaS stratifies prognosis and treatment response, offering a scalable and biologically grounded solution to accelerate precision oncology in routine care.