Towards real-world molecular pathology diagnosis of cancer with cross-modal AI
Xiaofei Wang, Yinyan Wang, Wanming Hu, Yupei Zhang, Xinke Zhang, Chen Lu, Jie Hu, Zeya Yan, Xing Liu, Hao Duan, Yonggao Mou, Tao Jiang, Mayen Briggs, Stephen Price, Chao Li · medRxiv · 2025
Abstract 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.