Construction and Validation of an Artificial Intelligence-Assisted Diagnostic Model for Glioma Based on Laboratory Indicators: A Single-Center Retrospective Cohort Study

Construction and Validation of an Artificial Intelligence-Assisted Diagnostic Model for Glioma Based on Laboratory Indicators: A Single-Center Retrospective Cohort Study | 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 Construction and Validation of an Artificial Intelligence-Assisted Diagnostic Model for Glioma Based on Laboratory Indicators: A Single-Center Retrospective Cohort Study Zheng Li, yizhou wei, huidong wang, dejian Wang, jianyong wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7825905/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The diagnosis of brain glioma relies on tissue biopsy and imaging examinations, which have invasiveness, sampling errors, and limitations in MRI differentiation. Biomarker research is mostly limited to single-index analysis, while artificial intelligence (AI) shows significant advantages in multi-dimensional data modeling. This study aims to construct an AI-assisted diagnostic model based on routine laboratory indicators to achieve non-invasive and accurate diagnosis and promote clinical transformation. Methods A retrospective analysis was performed on 71 laboratory indicators of 502 intracranial lesion patients (251 glioma cases and 251 control cases) from January 2006 to January 2024. Logistic regression, Softmax, and three-layer multi-layer perceptron (MLP) neural network were used for modeling, with model optimization through Min-Max normalization and SHAP value analysis. Results The MLP model showed the best performance, with a test set accuracy of 0.88, AUC of 0.933, sensitivity of 0.89, and specificity of 0.86. Key indicators were white blood cell count (SHAP 0.18), total bilirubin (0.15), triglycerides (0.13), and urine specific gravity (0.12), which were associated with tumor inflammation, liver metabolic reprogramming, lipid metabolism abnormalities, and water-electrolyte metabolism disorder, respectively. The model reduced the missed diagnosis rate from 23.7% to 5.8% in primary care hospitals and shortened the emergency diagnosis time to 2.5 hours. Conclusion This study first constructs a diagnostic model by integrating multi-dimensional laboratory indicators through AI, providing a new path for non-invasive screening of glioma. Multi-center studies are needed to verify its generalizability. Brain glioma Artificial intelligence Laboratory indicators Auxiliary diagnostic model Multi-layer perceptron SHAP value analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Nov, 2025 Editor invited by journal 13 Oct, 2025 Editor assigned by journal 12 Oct, 2025 Submission checks completed at journal 12 Oct, 2025 First submitted to journal 10 Oct, 2025 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-7825905","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546516767,"identity":"a8e617f3-4511-4404-9e41-4e6674d26fd9","order_by":0,"name":"Zheng Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Li","suffix":""},{"id":546516768,"identity":"c5601cad-221e-4130-b3ae-136d8d2a2240","order_by":1,"name":"yizhou wei","email":"","orcid":"","institution":"Binjiang Institute of Zhejiang 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Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Brain glioma, Artificial intelligence, Laboratory indicators, Auxiliary diagnostic model, Multi-layer perceptron, SHAP value analysis","lastPublishedDoi":"10.21203/rs.3.rs-7825905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7825905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe diagnosis of brain glioma relies on tissue biopsy and imaging examinations, which have invasiveness, sampling errors, and limitations in MRI differentiation. Biomarker research is mostly limited to single-index analysis, while artificial intelligence (AI) shows significant advantages in multi-dimensional data modeling. This study aims to construct an AI-assisted diagnostic model based on routine laboratory indicators to achieve non-invasive and accurate diagnosis and promote clinical transformation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective analysis was performed on 71 laboratory indicators of 502 intracranial lesion patients (251 glioma cases and 251 control cases) from January 2006 to January 2024. Logistic regression, Softmax, and three-layer multi-layer perceptron (MLP) neural network were used for modeling, with model optimization through Min-Max normalization and SHAP value analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe MLP model showed the best performance, with a test set accuracy of 0.88, AUC of 0.933, sensitivity of 0.89, and specificity of 0.86. Key indicators were white blood cell count (SHAP 0.18), total bilirubin (0.15), triglycerides (0.13), and urine specific gravity (0.12), which were associated with tumor inflammation, liver metabolic reprogramming, lipid metabolism abnormalities, and water-electrolyte metabolism disorder, respectively. The model reduced the missed diagnosis rate from 23.7% to 5.8% in primary care hospitals and shortened the emergency diagnosis time to 2.5 hours.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study first constructs a diagnostic model by integrating multi-dimensional laboratory indicators through AI, providing a new path for non-invasive screening of glioma. Multi-center studies are needed to verify its generalizability.\u003c/p\u003e","manuscriptTitle":"Construction and Validation of an Artificial Intelligence-Assisted Diagnostic Model for Glioma Based on Laboratory Indicators: A Single-Center Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 17:26:54","doi":"10.21203/rs.3.rs-7825905/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-10T14:22:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-13T15:51:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-13T01:03:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-13T01:03:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-10-10T10:29:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29ec6874-b62f-4e94-a436-1676fd7b0c2f","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T17:26:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 17:26:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7825905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7825905","identity":"rs-7825905","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}