MULTIMODAL DEEP LEARNING INTEGRATING GENOMICS, IMAGING, AND ELECTRONIC HEALTH RECORDS FOR EARLY CANCER DIAGNOSIS: TRANSFORMER ARCHITECTURES, CROSS-MODAL ATTENTION, AND CLINICAL TRANSLATION

The early diagnosis of pancreatic and ovarian cancers remains one of the most formidable challenges in oncology, with both malignancies characterized by asymptomatic progression, late-stage presentation, and poor survival outcomes. Multimodal deep learning—integrating genomics, medical imaging, and longitudinal electronic health records—offers a transformative pathway toward earlier detection by capturing complementary biological and clinical signals that single-modality approaches cannot access. This review critically examines the state-of-the-art in multimodal deep learning for early cancer diagnosis, with particular emphasis on transformer-based architectures and cross-modal attention mechanisms that enable the fusion of heterogeneous medical data. We evaluate recent advances in handling missing modalities—a pervasive challenge in real-world clinical settings—through imputation, modality dropout, and joint learning strategies. The role of explainable artificial intelligence techniques, including SHAP and LIME, in rendering multimodal predictions clinically interpretable is critically assessed. We further examine the formidable barrier of data silos across multi-center hospitals and the emerging promise of federated learning for privacy-preserving collaborative model development. Our analysis reveals that while transformer-based multimodal approaches consistently outperform unimodal baselines—with reported AUC improvements of 0.072 to 0.119—significant challenges persist in prospective validation, cross-population generalizability, and real-time clinical deployment. We identify critical research gaps, including the need for standardized multimodal benchmarks, robust handling of temporal irregularities, and integration of uncertainty quantification into clinical decision support systems.