Pre-treatment multimodal artificial intelligence for prognostic stratification in locally advanced rectal cancer

Pre-treatment multimodal artificial intelligence for prognostic stratification in locally advanced rectal cancer | 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 Pre-treatment multimodal artificial intelligence for prognostic stratification in locally advanced rectal cancer Peiqi Zhuang, Shenghua Cheng, Yandong Zhao, Hongtao Kang, Chongbao Sun, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7397345/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 Accurate prognostic assessment before neoadjuvant chemoradiotherapy remains challenging for locally advanced rectal cancer (LARC), limiting personalised treatment decisions. Here, we develop the Integrated Multimodal Prognostic Assessment for Locally Advanced Rectal Cancer Neoadjuvant Chemoradiotherapy (IMPACT), an artificial intelligence framework employing bidirectional multimodal attention mechanisms to capture cross-modal feature interactions and integrating pre-treatment pelvic magnetic resonance imaging, pathological biopsy whole slide images, and clinical information from 752 LARC patients across two independent centres. IMPACT achieves C-indexes of 0.805 for overall survival and 0.760 for disease-free survival, significantly outperforming the Guideline-based Imaging Risk Score (0.712 and 0.697, respectively). High-risk patients demonstrate 8.3-fold increased mortality risk and 6.5-fold increased recurrence risk compared to low-risk patients. External validation maintains robust performance with preserved risk stratification capability. Systematic ablation studies confirm the incremental value of trimodal fusion over single-modality approaches. IMPACT enables accurate pre-treatment prognostic stratification, facilitating evidence-based treatment intensification for high-risk patients and de-escalation strategies for low-risk cases in clinical practice. Biological sciences/Cancer Health sciences/Oncology Locally advanced rectal cancer Neoadjuvant chemoradiotherapy Artificial intelligence Survival analysis Magnetic resonance imaging Digital pathology Prognosis stratification Multimodal Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInfo.docx 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. 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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-7397345","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":505505276,"identity":"b328f21f-0660-4cad-8554-a8750bddd2b3","order_by":0,"name":"Peiqi Zhuang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peiqi","middleName":"","lastName":"Zhuang","suffix":""},{"id":505505278,"identity":"353a5a0a-e7f1-4be5-bb69-4f410d1903e6","order_by":1,"name":"Shenghua Cheng","email":"","orcid":"","institution":"Southern Medical 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