Time-dependent diffusion MRI for assessing tumor microstructure and prognostic risk factors in cervical cancer

Time-dependent diffusion MRI for assessing tumor microstructure and prognostic risk factors in cervical 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 Research Article Time-dependent diffusion MRI for assessing tumor microstructure and prognostic risk factors in cervical cancer Tianhui Zhang, Weixiong Fan, Kuiyuan Liu, Haoan Xu, Wenbiao Zhu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9328885/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 Purpose: To evaluate the utility of td-dMRI for noninvasively characterizing tumor microstructure and its potential value in prediction of prognostic risk factors for cervical cancer. Materials and methods: In this prospective study, 117 women with suspected cervical cancer underwent td-dMRI on a 3T scanner between January 2024 and February 2025. Microstructural parameters including intracellular volume fraction ( f in ), cell diameter ( d ), intracellular diffusivity ( D in ), extracellular diffusivity ( D ex ), intracellular water exchange rate ( k in ), intracellular water exchange time (τ) were derived using a two-compartment model, and multiple ADCs were obtained. Statistical analysis included inter-reader agreement, ROC, and logistic regression. Histologic validation was performed on H&E-stained slides. Results: Td-dMRI parameter d differentiated pathological type (AUC, 0.72) and Lymphovascular space involvement (LVSI) (AUC, 0.74), and independently predicted LVSI (OR, 1.523; p = 0.007). Parameter τ showed the strongest performance for histology grade (AUC, 0.82). Combining td-dMRI parameters with conventional diffusion metrics further improved discrimination of pathological type (AUC, 0.77), histology grade (AUC 0.82) and LVSI (AUC 0.79). Parameter d correlated well with the pathological ground truth with r = 0.595, p < .001. Conclusion: Td-dMRI yields noninvasive imaging biomarkers reflecting tumor microstructure, with the potential to enable risk stratification and guide individualized treatment planning in cervical cancer. Cervical cancer Time-dependent diffusion MRI Tumor microstructure Lymphovascular space involvement Figures Figure 1 Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.rar Figure S1 The correlations between microstructural parameters derived from time-dependent diffusion MRI and results of H&E-stained slices-based microstructural properties (n = 35). Correlation between diameter from time-dependent diffusion MRI parameters cellularity (A), intracellular fraction (B) and pathological examination-based microstructural properties. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 26 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 06 Apr, 2026 First submitted to journal 05 Apr, 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. 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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-9328885","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620640625,"identity":"07559d75-2d10-4a9b-9312-06e0c7561d6a","order_by":0,"name":"Tianhui Zhang","email":"","orcid":"","institution":"Meizhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianhui","middleName":"","lastName":"Zhang","suffix":""},{"id":620640628,"identity":"2a1d6b56-5692-4ea5-8e13-3a9cde39e9bb","order_by":1,"name":"Weixiong Fan","email":"","orcid":"","institution":"Meizhou People's 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(A) H\u0026amp;E-stained-stained image (scale bar: 100 μm) shows pathologic specimens from one participant. (B) The overlay of H\u0026amp;E-stained-stained image (scale bar: 100 μm) shows nuclei that were segmented by a pretrained pretrained deep learning network. (C) Correlation between diameter from time-dependent diffusion MRI and nuclei diameter from the pathological examination-based microstructural properties.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9328885/v1/6c58543632e0f3d213b839a1.png"},{"id":107485522,"identity":"b0566b01-0569-4158-9bf0-92d4b6fa20a7","added_by":"auto","created_at":"2026-04-22 02:35:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886962,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptsubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9328885/v1_covered_b1047377-2b00-47dc-837f-8a3a4713be07.pdf"},{"id":107482917,"identity":"44a1564a-0b2c-4e89-8f42-d172c80f0089","added_by":"auto","created_at":"2026-04-22 02:25:34","extension":"rar","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":62498,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003e The correlations between microstructural parameters derived from time-dependent diffusion MRI and results of H\u0026amp;E-stained slices-based microstructural properties (n = 35). Correlation between diameter from time-dependent diffusion MRI parameters cellularity (A), intracellular fraction (B) and pathological examination-based microstructural properties.\u003c/p\u003e","description":"","filename":"Supplementarymaterial.rar","url":"https://assets-eu.researchsquare.com/files/rs-9328885/v1/022a33aa0c764df3ff7ad0d7.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-dependent diffusion MRI for assessing tumor microstructure and prognostic risk factors in cervical cancer","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cervical cancer, Time-dependent diffusion MRI, Tumor microstructure, Lymphovascular space involvement","lastPublishedDoi":"10.21203/rs.3.rs-9328885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9328885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e To evaluate the utility of td-dMRI for noninvasively characterizing tumor microstructure and its potential value in prediction of prognostic risk factors for cervical cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods:\u003c/strong\u003e In this prospective study, 117 women with suspected cervical cancer underwent td-dMRI on a 3T scanner between January 2024 and February 2025. Microstructural parameters including intracellular volume fraction (\u003cem\u003ef\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e), cell diameter (\u003cem\u003ed\u003c/em\u003e), intracellular diffusivity (\u003cem\u003eD\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e), extracellular diffusivity (\u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e), intracellular water exchange rate (\u003cem\u003ek\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e), intracellular water exchange time (τ) were derived using a two-compartment model, and multiple ADCs were obtained. Statistical analysis included inter-reader agreement, ROC, and logistic regression. Histologic validation was performed on H\u0026amp;E-stained slides.\u003cstrong\u003e\u003cbr\u003e\nResults: \u003c/strong\u003eTd-dMRI parameter \u003cem\u003ed\u003c/em\u003e differentiated pathological type (AUC, 0.72) and Lymphovascular space involvement (LVSI) (AUC, 0.74), and independently predicted LVSI (OR, 1.523; \u003cem\u003ep\u003c/em\u003e = 0.007). Parameter τ showed the strongest performance for histology grade (AUC, 0.82). Combining td-dMRI parameters with conventional diffusion metrics further improved discrimination of pathological type (AUC, 0.77), histology grade (AUC 0.82) and LVSI (AUC 0.79). Parameter \u003cem\u003ed\u003c/em\u003e correlated well with the pathological ground truth with r = 0.595, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eTd-dMRI yields noninvasive imaging biomarkers reflecting tumor microstructure, with the potential to enable risk stratification and guide individualized treatment planning in cervical cancer.\u003c/p\u003e","manuscriptTitle":"Time-dependent diffusion MRI for assessing tumor microstructure and prognostic risk factors in cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 07:18:13","doi":"10.21203/rs.3.rs-9328885/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"244309034620195098613167280679758825899","date":"2026-04-26T12:53:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T02:45:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T02:57:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T02:57:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2026-04-06T00:40:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"399443e2-e7dc-4c5e-a4b0-6c4da8b68332","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T07:18:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 07:18:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9328885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9328885","identity":"rs-9328885","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}