Towards Precise Treatment Response and Overall Survival Prediction in Gastric Cancer via Interpretable and Robust Multimodal Data Integration

Towards Precise Treatment Response and Overall Survival Prediction in Gastric Cancer via Interpretable and Robust Multimodal Data Integration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Towards Precise Treatment Response and Overall Survival Prediction in Gastric Cancer via Interpretable and Robust Multimodal Data Integration Hao Chen, Fengtao Zhou, Yingxue XU, Yanfen Cui, Shenyan Zhang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5081690/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Gastric cancer (GC) emerges as a significant global malignancy, ranking as the fifth most prevalent cancer in 2022. The integration of multimodal data provides a more holistic understanding of the disease, enhancing predictions of treatment response and overall survival for GC patients. Nevertheless, the inherent heterogeneity and potential incompleteness of multimodal data pose formidable challenges for effective integration and in-depth analysis. Furthermore, the lack of interpretability in the prediction models hinders their trustworthiness and acceptance in clinical practice. Therefore, this study presented an interpretable and robust multimodal data integration framework for GC analysis (iMD4GC), facilitating effective integration of diverse data sources to predict treatment response and overall survival. Three multimodal datasets were collected for model evaluation: GastricRes (n=698) for response prediction, GastricSur (n=801) for survival analysis, and TCGA-STAD (n=400) for further survival analysis. The iMD4GC achieved an area under the receiver operating characteristic curve (AUC) of 0.803 (95% CI 0.680-0.925) on GastricRes, a concordance index (c-index) of 0.715 (95% CI 0.646-0.784) and a time-dependent AUC of 0.739 (95% CI 0.644-0.833) on GastricSur, and a cindex of 0.661 (95% CI 0.504-0.818) and a time-dependent AUC of 0.690 (95% CI 0.500-0.881) on TCGA-STAD. The comparative analysis underscores the reliability of iMD4GC in integrating diverse multimodal data and addressing missing modalities. Further interpretability analysis identified pivotal factors across various data modalities, validating the alignment with clinical expertise and providing essential insights for informed decision-making in clinical contexts. The robustness and scalability hold significant implications for clinical practice, enabling precision oncology through artificial intelligence and multimodal data integration. Biological sciences/Computational biology and bioinformatics Biological sciences/Cancer Gastric Cancer Multimodal Data Integration Neoadjuvant Chemotherapy Response Overall Survival Analysis Interpretable Prediction Model Full Text Additional Declarations There is NO Competing Interest. Supplementary Files COMMSMED251329Tsupplementary.pdf Appendix: Towards Precise Treatment Response and Overall Survival Prediction in Gastric Cancer via Interpretable and Robust Multimodal Data Integration Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5081690","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490002795,"identity":"fdb93e4a-d34d-4551-b589-7b1a10b018d1","order_by":0,"name":"Hao 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