Selma Mtoor, Niki Rashidian, Nouredin Messaoudi, Vincent Grasso, Floriane Noël, Michele Steindler, Derar Jaradat, Isabella Frigerio, G. Butturini, RS Croner, Karol Rawicz-Pruszynski, Giulia Capelli, Gaya Spolverato, Marc G. Besselink, Takeaki Ishizawa, Élie Chouillard, Mohammad Abu-Hilal, Ulf D. Kahlert, Ibrahim Dagher, Andrew A. Gumbs
BACKGROUND: Multimodal AI integration across genomics, radiomics, and pathomics is rapidly evolving in oncology, but evidence remains heterogeneous and unevenly distributed across modalities. OBJECTIVE: To map empirical studies integrating two or more -omic modalities, summarize integration and validation approaches, and identify gaps informing future directions toward surgomics. METHODS: We conducted a scoping review in accordance with PRISMA-ScR, searching PubMed, Ovid, Wiley Online Library, and Google Scholar for English-language studies published from January 2020 to 5 March 2025. We charted study characteristics, modalities combined, fusion strategies, AI model categories, validation approaches, and reported performance metrics as presented by the original studies. RESULTS: = 1078 total participants across reported studies), most focusing on radiomics-pathomics integration; fewer incorporated genomics, and tri-modal fusion was uncommon. Studies varied widely in clinical tasks, endpoints, preprocessing, and validation, limiting direct comparability. CONCLUSIONS: The mapped evidence indicates growing methodological activity in radiopathomics and cross-scale association modeling, while tri-modal pipelines and clinically deployable multimodal workflows remain underdeveloped. Surgomics is presented as a conceptual, staged roadmap informed by these gaps rather than a current clinical capability.