AI-Driven Radiopathomics: Integrating Radiology and Digital Pathology for Personalized Cancer Care

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. Modern oncology increasingly relies on complementary information derived from radiological imaging and digital pathology to characterize tumor biology, guide therapeutic decision-making, and monitor disease progression. Traditionally, these two domains have been analyzed independently despite describing different yet interconnected aspects of cancer biology. Recent advances in artificial intelligence (AI) have introduced the field of radiopathomics, which integrates radiology, digital pathology, multi-omics, laboratory biomarkers, and longitudinal clinical information into unified computational frameworks that support precision oncology. Machine learning, deep learning, transformer architectures, graph neural networks, multimodal foundation models, self-supervised learning, and generative AI have enabled automated extraction of quantitative imaging biomarkers and histomorphological features capable of improving cancer diagnosis, prognostic prediction, molecular characterization, treatment optimization, immunotherapy response prediction, and clinical decision support. AI-driven radiopathomics provides comprehensive characterization of tumor phenotype by combining macroscopic anatomical information with microscopic tissue architecture, thereby facilitating personalized cancer management. Despite substantial technological progress, challenges remain regarding multimodal data harmonization, computational scalability, explainability, interoperability, regulatory validation, and equitable clinical implementation. This review presents a comprehensive overview of AI-driven radiopathomics, emphasizing computational principles, current clinical applications, emerging innovations, and future perspectives for personalized cancer care.