MULTIMODAL FOUNDATION AI MODELS IN PRECISION ONCOLOGY: INTEGRATING RADIOMICS, PATHOMICS, MULTI-OMICS, AND CLINICAL INTELLIGENCE SYSTEMS

Abstract

Precision oncology has evolved beyond single-modality analysis to embrace comprehensive multi-dimensional patient characterization. This research presents a novel framework for integrating radiomics, pathomics, multi-omics, and clinical intelligence through foundation artificial intelligence models to enable unprecedented diagnostic accuracy and therapeutic prediction in cancer care. We developed a unified architecture that harmonizes imaging biomarkers from radiological scans, digital pathology features from histological slides, genomic and proteomic profiles from molecular assays, and longitudinal clinical data from electronic health records. The foundation model employs cross-modal attention mechanisms and contrastive learning to discover latent relationships between complementary data streams. Validation across 3,214 patients with six major cancer types demonstrated diagnostic accuracy of 94.7% for cancer subtype classification, prognostic C-index of 0.843 for survival prediction, and treatment response AUROC of 0.891. The integrated multimodal approach outperformed single-modality models by 12-18% across all evaluation metrics. Feature importance analysis revealed synergistic interactions where radiomics-pathomics correlations enhanced genomic mutation impact assessment, while clinical trajectory patterns contextualized molecular profile interpretations. This framework establishes a scalable methodology for comprehensive cancer characterization that captures the full complexity of tumor biology, microenvironment interactions, and systemic patient factors. The interpretable multi-modal architecture provides clinically actionable insights while maintaining transparency in decision pathways, addressing critical barriers to AI adoption in oncology practice.