Predicting High-Risk Colorectal Polyps Using Pre-Colonoscopy Features: Machine Learning Model Development and Validation

Predicting High-Risk Colorectal Polyps Using Pre-Colonoscopy Features: Machine Learning Model Development and Validation | 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 Predicting High-Risk Colorectal Polyps Using Pre-Colonoscopy Features: Machine Learning Model Development and Validation Basheer Qolomany, Mrinalini Deverapall, Adeyinka Laiyemo, Zaki Sherif, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8627377/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Purpose :Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whether non-invasive features available prior to colonoscopy can help identify patients at higher risk. Such approaches may enhance clinical decision-making by prioritizing surveillance for individuals most likely to harbor high-risk polyps, when colonoscopy resources are limited while potentially reducing unnecessary procedures in lower-risk patients. Importantly, the use of non-invasive, pre-procedural information may also help promote more equitable access to risk stratification, particularly in settings where colonoscopy resources are limited or unevenly distributed. We aimed to develop and externally validate machine learning models to predict high-risk colorectal polyps using only non-invasive, pre-colonoscopy demographic, clinical, and behavioral features in a diverse, predominantly African American, urban cohort. Methods : We conducted a retrospective cohort study using demographic, lifestyle, and comorbidity data from patients who underwent colonoscopy at Howard University Hospital to develop and validate several machine learning models, including neural networks, random forest, support vector machines (SVM), Naïve Bayes, logistic regression, decision trees, k-nearest neighbors (KNN), and XGBoost, for predicting high-risk colorectal polyps. High-risk polyps (HRP) were defined as villous or tubullovillous adenomas, high-grade dysplasia, polyps $\geq$ 10 mm in size, and/or the presence of $\geq$ 3 polyps per procedure; all other cases were classified as low-risk polyps (LRP). The dataset included 4,681 patients from 2015-2022 used for internal validation and 1,562 patients from 2023-2024 used for external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall area under the curve (PR-AUC), accuracy, precision, recall, and F1 score. Model interpretability and feature contribution were assessed using SHapley Additive exPlanations (SHAP). Results : Overall predictive performance was moderate using non-invasive pre-colonoscopy features. The neural network demonstrated the strongest overall discrimination, achieving the highest internal validation performance (ROC-AUC 0.78, PR-AUC 0.75, accuracy 0.72), but showed reduced performance in the external cohort (ROC-AUC 0.67, accuracy 0.66), suggesting potential overfitting or temporal feature drift. In contrast, simpler models including Naïve Bayes, SVM, and XGBoost exhibited lower internal performance (ROC-AUC 0.54-0.59) but more stable generalization to the external cohort (ROC-AUC 0.52-0.63; accuracy approximately 0.53-0.60). Model interpretability analysis using SHAP identified age, smoking status, sex, occupation, race, colonoscopy indication, and family history of colorectal cancer as the most influential predictors, highlighting contributions from both traditional clinical and sociodemographic factors. Conclusions :Prediction of HRP using routine pre-colonoscopy data is feasible but demonstrates limited generalizability across cohorts. These findings highlight the clinical potential and limitations of pre-procedural risk modeling, especially in diverse, underserved populations. Integration of additional data modalities may be required to achieve clinically robust, and equitable prediction tools. Colorectal cancer screening Colonoscopy Machine learning Risk stratification Adenomatous polyps Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 26 Jan, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 17 Jan, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8627377","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581983153,"identity":"0e9d0522-c437-4025-91a6-e74e9ce11a21","order_by":0,"name":"Basheer 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Colorectal cancer screening, Colonoscopy, Machine learning, Risk stratification, Adenomatous polyps","lastPublishedDoi":"10.21203/rs.3.rs-8627377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8627377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e:Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whether non-invasive features available prior to colonoscopy can help identify patients at higher risk. Such approaches may enhance clinical decision-making by prioritizing surveillance for individuals most likely to harbor high-risk polyps, when colonoscopy resources are limited while potentially reducing unnecessary procedures in lower-risk patients. Importantly, the use of non-invasive, pre-procedural information may also help promote more equitable access to risk stratification, particularly in settings where colonoscopy resources are limited or unevenly distributed. We aimed to develop and externally validate machine learning models to predict high-risk colorectal polyps using only non-invasive, pre-colonoscopy demographic, clinical, and behavioral features in a diverse, predominantly African American, urban cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conducted a retrospective cohort study using demographic, lifestyle, and comorbidity data from patients who underwent colonoscopy at Howard University Hospital to develop and validate several machine learning models, including neural networks, random forest, support vector machines (SVM), Naïve Bayes, logistic regression, decision trees, k-nearest neighbors (KNN), and XGBoost, for predicting high-risk colorectal polyps. High-risk polyps (HRP) were defined as villous or tubullovillous adenomas, high-grade dysplasia, polyps $\\geq$ 10 mm in size, and/or the presence of $\\geq$ 3 polyps per procedure; all other cases were classified as low-risk polyps (LRP). The dataset included 4,681 patients from 2015-2022 used for internal validation and 1,562 patients from 2023-2024 used for external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall area under the curve (PR-AUC), accuracy, precision, recall, and F1 score. Model interpretability and feature contribution were assessed using SHapley Additive exPlanations (SHAP).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Overall predictive performance was moderate using non-invasive pre-colonoscopy features. The neural network demonstrated the strongest overall discrimination, achieving the highest internal validation performance (ROC-AUC 0.78, PR-AUC 0.75, accuracy 0.72), but showed reduced performance in the external cohort (ROC-AUC 0.67, accuracy 0.66), suggesting potential overfitting or temporal feature drift. In contrast, simpler models including Naïve Bayes, SVM, and XGBoost exhibited lower internal performance (ROC-AUC 0.54-0.59) but more stable generalization to the external cohort (ROC-AUC 0.52-0.63; accuracy approximately 0.53-0.60). Model interpretability analysis using SHAP identified age, smoking status, sex, occupation, race, colonoscopy indication, and family history of colorectal cancer as the most influential predictors, highlighting contributions from both traditional clinical and sociodemographic factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:Prediction of HRP using routine pre-colonoscopy data is feasible but demonstrates limited generalizability across cohorts. These findings highlight the clinical potential and limitations of pre-procedural risk modeling, especially in diverse, underserved populations. Integration of additional data modalities may be required to achieve clinically robust, and equitable prediction tools.\u003c/p\u003e","manuscriptTitle":"Predicting High-Risk Colorectal Polyps Using Pre-Colonoscopy Features: Machine Learning Model Development and Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 06:06:36","doi":"10.21203/rs.3.rs-8627377/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T10:00:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191240408183271869891680337235381939445","date":"2026-04-19T20:58:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-26T17:33:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T20:07:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T05:24:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Digestive Diseases and Sciences","date":"2026-01-17T16:31:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"digestive-diseases-and-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ddsj","sideBox":"Learn more about [Digestive Diseases and Sciences](http://link.springer.com/journal/10620)","snPcode":"10620","submissionUrl":"https://submission.nature.com/new-submission/10620/3","title":"Digestive Diseases and Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7ba3d411-baf1-4803-a0d1-fb4402c8542f","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T10:00:56+00:00","index":28,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T06:06:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 06:06:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8627377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8627377","identity":"rs-8627377","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}