Prognostic analysis based on multi-features calculation and clinical information fusion of colorectal cancer whole slide pathological image

Prognostic analysis based on multi-features calculation and clinical information fusion of colorectal cancer whole slide pathological image | 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 Research Article Prognostic analysis based on multi-features calculation and clinical information fusion of colorectal cancer whole slide pathological image Chengfei Cai, Yangshu Zhou, Xiangxue Wang, Yiping Jiao, Li Liang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3230297/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 Background: Colorectal cancer (CRC) is a malignant tumor within digestive tract with both high incidence rate and and mortality. Early detection and intervention could improve patient clinical outcome and survival. Methods: This study computationally investigate a set of prognostic tissue and celluer features from diagnostic tissue slide. With the combination of clinical prognostic variable, the pathological image features could predict the prognosis in CRC patients.ur CRC prognosis prediction pipeline is sequentially consisted of three modules: (1) A DeepTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologist; (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape and hidden features from deep network; (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC. Results: Experimental results suggest that each group of features has a certain relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77. Conclusion: This paper constructs a model that can predict postoperative survival of patients by using image features and clinical information.Some features were found to be associated with the prognosis and survival of patients. Whole Slide Image Deep Convolutional Networks Multi Tissue Segmentation Feature Extraction and Selection Prognostic Survival Analysis Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.pdf 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. 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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-3230297","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":223937955,"identity":"782ac78a-a3b7-4af9-9c89-54a325cacf33","order_by":0,"name":"Chengfei 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Networks, Multi Tissue Segmentation, Feature Extraction and Selection, Prognostic Survival Analysis","lastPublishedDoi":"10.21203/rs.3.rs-3230297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3230297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Colorectal cancer (CRC) is a malignant tumor within digestive tract with both high incidence rate and and mortality. Early detection and intervention could improve patient clinical outcome and survival.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study computationally investigate a set of prognostic tissue and celluer features from diagnostic tissue slide. 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