Enhancing Bone Metastasis CT Report Analysis: A Comparison of Local and Proprietary Large Language Models for Privacy and Resource Efficiency

Enhancing Bone Metastasis CT Report Analysis: A Comparison of Local and Proprietary Large Language Models for Privacy and Resource Efficiency | 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 Enhancing Bone Metastasis CT Report Analysis: A Comparison of Local and Proprietary Large Language Models for Privacy and Resource Efficiency Zhuo Li, Mengfei Wang, Ao Liu, Yao Zeng, Bichong Yan, Zhongzheng Cao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7716337/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in BMC Health Services Research → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Patients with metastatic bone tumors often suffer pathological fractures. CT reports contain critical diagnostic information, but their free-text nature makes information extraction inefficient, increases physicians’ documentation burden, and raises privacy risks. Methods: This study analyzed 300 de-identified CT reports of metastatic bone tumors and compared seven large language models (LLMs), including two proprietary models (GPT-4o, Gemini Pro) and five open-source locally deployable models (DeepSeek-V3, Qwen2-72B, LLaMA3.1-70B, Gemma2-9B, WiNGPT2-9B). Tasks included identifying pathological fractures, extracting fracture sites, and extracting metastatic sites. Expert annotations served as the reference standard. Evaluation metrics included accuracy, recall, precision, F1 score, AUC, and Cohen’s Kappa. Results: All models achieved accuracies above 80%. Open-source models such as Qwen2-72B and WiNGPT2-9B demonstrated comparable performance to GPT-4o ( \((p > 0.05)\) ) while offering advantages in privacy protection and local deployment. Domain-specific fine-tuning further improved the performance of smaller models (e.g., WiNGPT2-9B) in resource-constrained environments. Conclusion: Locally deployable and fine-tuned open-source LLMs can assist physicians by rapidly extracting key findings from CT reports under strict privacy protection. They reduce documentation burden, enhance diagnostic efficiency, and provide a sustainable AI pathway for healthcare systems, especially in privacy-sensitive or resource-limited settings. Large Language Models (LLMs) Metastatic Bone Tumors CT Reports Privacy Protection Resource Efficiency Clinical Decision Support Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2026 Read the published version in BMC Health Services Research → Version 1 posted Editorial decision: Revision requested 08 Dec, 2025 Reviews received at journal 05 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers invited by journal 28 Oct, 2025 Editor invited by journal 24 Oct, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 25 Sep, 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-7716337","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540361886,"identity":"b571733d-ca90-4851-97ae-c8fe973a5579","order_by":0,"name":"Zhuo Li","email":"","orcid":"","institution":"Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"","lastName":"Li","suffix":""},{"id":540361887,"identity":"a2668e99-c1c9-48ab-86be-27fbaf3e23e9","order_by":1,"name":"Mengfei Wang","email":"","orcid":"","institution":"Shanghai 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Efficiency","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Large Language Models (LLMs), Metastatic Bone Tumors, CT Reports, Privacy Protection, Resource Efficiency, Clinical Decision Support","lastPublishedDoi":"10.21203/rs.3.rs-7716337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7716337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003ePatients with metastatic bone tumors often suffer pathological fractures. CT reports contain critical diagnostic information, but their free-text nature makes information extraction inefficient, increases physicians\u0026rsquo; documentation burden, and raises privacy risks.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003e This study analyzed 300 de-identified CT reports of metastatic bone tumors and compared seven large language models (LLMs), including two proprietary models (GPT-4o, Gemini Pro) and five open-source locally deployable models (DeepSeek-V3, Qwen2-72B, LLaMA3.1-70B, Gemma2-9B, WiNGPT2-9B). Tasks included identifying pathological fractures, extracting fracture sites, and extracting metastatic sites. Expert annotations served as the reference standard. Evaluation metrics included accuracy, recall, precision, F1 score, AUC, and Cohen\u0026rsquo;s Kappa.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eAll models achieved accuracies above 80%. Open-source models such as Qwen2-72B and WiNGPT2-9B demonstrated comparable performance to GPT-4o (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((p \u0026gt; 0.05)\\)\u003c/span\u003e\u003c/span\u003e) while offering advantages in privacy protection and local deployment. Domain-specific fine-tuning further improved the performance of smaller models (e.g., WiNGPT2-9B) in resource-constrained environments.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eLocally deployable and fine-tuned open-source LLMs can assist physicians by rapidly extracting key findings from CT reports under strict privacy protection. They reduce documentation burden, enhance diagnostic efficiency, and provide a sustainable AI pathway for healthcare systems, especially in privacy-sensitive or resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Enhancing Bone Metastasis CT Report Analysis: A Comparison of Local and Proprietary Large Language Models for Privacy and Resource Efficiency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 02:51:43","doi":"10.21203/rs.3.rs-7716337/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-09T04:10:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T21:43:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T04:07:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300495151200834491892269257186204440251","date":"2025-11-26T21:20:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195025370519749048223036133722504658947","date":"2025-11-22T09:06:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-28T16:50:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-24T14:14:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T09:32:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T09:31:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-09-25T23:05:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90cc8d54-c09b-43bd-bb4c-77d0b80028b6","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:04:54+00:00","versionOfRecord":{"articleIdentity":"rs-7716337","link":"https://doi.org/10.1186/s12913-026-14350-3","journal":{"identity":"bmc-health-services-research","isVorOnly":false,"title":"BMC Health Services Research"},"publishedOn":"2026-03-19 15:58:51","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-11-07 02:51:43","video":"","vorDoi":"10.1186/s12913-026-14350-3","vorDoiUrl":"https://doi.org/10.1186/s12913-026-14350-3","workflowStages":[]},"version":"v1","identity":"rs-7716337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7716337","identity":"rs-7716337","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}