Reinforcement Learning-Optimized Personalized Cancer Treatment Strategies: A Case Study of Lung Cancer

Reinforcement Learning-Optimized Personalized Cancer Treatment Strategies: A Case Study of Lung 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 Reinforcement Learning-Optimized Personalized Cancer Treatment Strategies: A Case Study of Lung Cancer Chichun Zhou, Zhaocong Liu, Xinhui Li, Shuncheng Nai, Junpeng Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5262065/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 Personalized cancer treatment strategies (PCTS) tailor treatments on the basis of a patient’s health status, cancer type, and stage. By considering the evolving interactions of treatment options over time, PCTS seeks to balance cancer suppression with minimizing harm and maximizing therapeutic benefits. However, limited clinical trial resources limit the ability to explore optimal PCTSs fully through experimentation, presenting a significant challenge to their development. In this study, we introduce a "digital twin" model that integrates comprehensive patient health data, cancer characteristics, and individual treatment responses and employs reinforcement learning (RL) to identify the optimal PCTS. Using lung cancer as a case study, we calibrated model parameters for various demographic groups, cancer stages, and treatment options, utilizing real clinical data from the SEER dataset. The RL-optimized PCTS significantly outperformed traditional clinician decisions, leading to notable improvements in patient survival. For example, among women aged 45--64 years with stage IIIA, IIIB, IVA, and IVB lung cancer, survival increased by 46%, 59%, 23%, and 149%, respectively. Similarly, for men aged 45--64 years, survival improved by 108%, 97%, 40%, and 62%, respectively, across the same stages. This study lays a critical foundation for the use of AI in optimizing PCTS and paves the way for further research and clinical applications. Biological sciences/Cancer/Cancer models Biological sciences/Cancer/Cancer therapy Personalized cancer treatment Personalized cancer treatment strategies Reinforcement learning Cancer treatment model Digital twin patient Full Text Additional Declarations No competing interests reported. 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. 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-5262065","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":366558364,"identity":"dd38ac00-0002-4606-82e7-aaef4b4d0cae","order_by":0,"name":"Chichun Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCSBmbJBgMABSD3gYLHiAfAOitTAb8DBIEK0FrIwNqB7EJaBFfnbzMYmfOyzkzSVyj1W8qZGQYWBv3ibBUHMHpxbGOcfSJHvPSBjunJGXdnPOMaBFPMfKJBiOPcOphVkix0yasU0iweBGjtltHrDbcswkGBsO49TChqylmOcfUIv8G/xaeJC1MPO2gWzhwa9FQiIt2bK3TcJww5k3xpJz+yR42HjSii0SjuHWIj8j+eCNn2118gbHcww/vPlmY8/PfnjjjQ81uLVg8R2ISCBBwygYBaNgFIwCTAAARLtHF+CdfqwAAAAASUVORK5CYII=","orcid":"","institution":"Dali University","correspondingAuthor":true,"prefix":"","firstName":"Chichun","middleName":"","lastName":"Zhou","suffix":""},{"id":366558365,"identity":"939f7bc3-491b-44c1-8a9d-a5ec7b959ab6","order_by":1,"name":"Zhaocong Liu","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Zhaocong","middleName":"","lastName":"Liu","suffix":""},{"id":366558366,"identity":"7637b9d8-2197-4062-8744-ecd9fb0cdef3","order_by":2,"name":"Xinhui Li","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Xinhui","middleName":"","lastName":"Li","suffix":""},{"id":366558367,"identity":"771cc434-d2ec-4b43-8592-79141cb7cb71","order_by":3,"name":"Shuncheng Nai","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Shuncheng","middleName":"","lastName":"Nai","suffix":""},{"id":366558368,"identity":"2cbf50fa-bdb6-4a03-806c-0e200955bd67","order_by":4,"name":"Junpeng Zhang","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Junpeng","middleName":"","lastName":"Zhang","suffix":""},{"id":366558369,"identity":"f204ec78-b631-4fa6-adf0-7997a02cf073","order_by":5,"name":"Yuanping Lan","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Yuanping","middleName":"","lastName":"Lan","suffix":""},{"id":366558370,"identity":"422fe800-97ac-4799-841c-ba0c0ac5a583","order_by":6,"name":"Lijuan Li","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Li","suffix":""},{"id":366558371,"identity":"e55c950a-145c-4391-bab0-4760707d4c47","order_by":7,"name":"Yi Liu","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liu","suffix":""},{"id":366558372,"identity":"d919d944-f63b-4ae3-87ec-0fddfa299a9c","order_by":8,"name":"Bin Wang","email":"","orcid":"","institution":"The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":366558373,"identity":"b2debaae-ceeb-4d7b-bd69-6bda880d9202","order_by":9,"name":"Yaling Liu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yaling","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-10-14 14:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5262065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5262065/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66852507,"identity":"d8be2329-2077-4218-aee4-16562791a7f3","added_by":"auto","created_at":"2024-10-17 07:14:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2204448,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5262065/v1_covered_b3f33878-d766-41d0-89d6-8c21b1fab742.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reinforcement Learning-Optimized Personalized Cancer Treatment Strategies: A Case Study of Lung Cancer","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Personalized cancer treatment, Personalized cancer treatment strategies, Reinforcement learning, Cancer treatment model, Digital twin patient","lastPublishedDoi":"10.21203/rs.3.rs-5262065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5262065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePersonalized cancer treatment strategies (PCTS) tailor treatments on the basis of a patient’s health status, cancer type, and stage. By considering the evolving interactions of treatment options over time, PCTS seeks to balance cancer suppression with minimizing harm and maximizing therapeutic benefits. However, limited clinical trial resources limit the ability to explore optimal PCTSs fully through experimentation, presenting a significant challenge to their development. In this study, we introduce a \"digital twin\" model that integrates comprehensive patient health data, cancer characteristics, and individual treatment responses and employs reinforcement learning (RL) to identify the optimal PCTS. Using lung cancer as a case study, we calibrated model parameters for various demographic groups, cancer stages, and treatment options, utilizing real clinical data from the SEER dataset. The RL-optimized PCTS significantly outperformed traditional clinician decisions, leading to notable improvements in patient survival. For example, among women aged 45--64 years with stage IIIA, IIIB, IVA, and IVB lung cancer, survival increased by 46%, 59%, 23%, and 149%, respectively. Similarly, for men aged 45--64 years, survival improved by 108%, 97%, 40%, and 62%, respectively, across the same stages. This study lays a critical foundation for the use of AI in optimizing PCTS and paves the way for further research and clinical applications.\u003c/p\u003e","manuscriptTitle":"Reinforcement Learning-Optimized Personalized Cancer Treatment Strategies: A Case Study of Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 07:05:51","doi":"10.21203/rs.3.rs-5262065/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f57db49b-a996-4549-8b94-7255ac98d08b","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39000554,"name":"Biological sciences/Cancer/Cancer models"},{"id":39000555,"name":"Biological sciences/Cancer/Cancer therapy"}],"tags":[],"updatedAt":"2024-10-21T11:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 07:05:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5262065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5262065","identity":"rs-5262065","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}