Predictive imaging biomarkers on Whole-Body Diffusion Weighted MRI (WB-DWMRI) and 68 Ga-PSMA-PET/CT for 177 Lu-PSMA-Therapy in metastatic prostate cancer (mPC)

Predictive imaging biomarkers on Whole-Body Diffusion Weighted MRI (WB-DWMRI) and 68 Ga-PSMA-PET/CT for 177 Lu-PSMA-Therapy in metastatic prostate cancer (mPC) | 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 Predictive imaging biomarkers on Whole-Body Diffusion Weighted MRI (WB-DWMRI) and 68 Ga-PSMA-PET/CT for 177 Lu-PSMA-Therapy in metastatic prostate cancer (mPC) Minal Padden-Modi, Peter Dutey-Magni, Matthew Blackledge, Hoda Abdel-Aty, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8564323/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Purpose This study evaluates the utility of quantitative imaging biomarkers derived from WB-DWMRI and 68 Ga-PSMA-PET/CT in predicting lesion-level response to 177 Lu-PSMA-Therapy in mPC. Methods Twenty-two patients with mPC who underwent WB-DWMRI and 68 Ga-PSMA-PET/CT within three months before 177 Lu-PSMA therapy were identified. The PSMA SUV (mean, standard-deviation, peak) and corresponding DW Imaging (DWI) parameters (ADC mean, ADC kurtosis, volume) were extracted from five hottest lesions (highest SUVmean) on PSMA-PET/CT. Lesion response was assessed using modified PERCIST and MET-RADS-P criteria. Multilevel logistic regression and area under receiver operating characteristics (AUROC) analyses identified predictive biomarkers. Results 65 bone lesions and 30 lymph nodes were analysed pre- and post-therapy. Forty-four (68%) bone and 16 (53%) lymph nodes lesions responded to treatment. SUV mean and peak were almost identical (rank-correlation = 0.97) and had high predictive performance for response with AUROC of 0.74 (95% CI: 0.62–0.86) and 0.75 (95% CI: 0.61–0.88) respectively. Lesion volume showed good performance (AUROC = 0.69, 95% CI 0.57–0.82) and moderately correlated with SUVmean (rank-correlation = 0.43). Neither ADCmean (AUROC = 0.45, p = 0.47) or kurtosis (AUROC = 0.49, p = 0.171) were predictive. On regression modelling, a 1-unit volume increase, raised response odds by 4.3 (95% CI 0.7–27) in bone lesions, and 6.2 (95% CI 0.6–67) in lymph nodes ( p = 0.06). A 1-unit SUVmean increase raised response odds by 1.5 (95% CI 1.1–2.0) in bone lesions and 1.2 (95% CI 1.0–1.4) in lymph nodes ( p < 0.01). No evidence suggested combining volume with SUVmean enhanced predictive performance over SUVmean alone ( p = 0.58). Conclusions Baseline PSMA SUVmean, SUVpeak, and volume are promising predictive biomarkers for lesion-level response to 177 Lu-PSMA-Therapy. Combining functional and structural imaging biomarkers could improve treatment stratification and response assessment. Further validation in larger studies, alongside patient-level analysis and expansion beyond the five lesions is needed to refine predictive models for clinical application. 177Lu Prostate Cancer MRI PSMA/PET CT Predictive Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialforsubmissiontoeuropeanradiology.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 16 Jan, 2026 Editor assigned by journal 16 Jan, 2026 Submission checks completed at journal 15 Jan, 2026 First submitted to journal 09 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-8564323","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576862083,"identity":"3cc0f4cb-5248-42df-8c03-ed2fb6f1b51b","order_by":0,"name":"Minal 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metastatic prostate cancer (mPC)","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":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"177Lu, Prostate Cancer, MRI, PSMA/PET CT, Predictive ","lastPublishedDoi":"10.21203/rs.3.rs-8564323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8564323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study evaluates the utility of quantitative imaging biomarkers derived from WB-DWMRI and \u003csup\u003e68\u003c/sup\u003eGa-PSMA-PET/CT in predicting lesion-level response to \u003csup\u003e177\u003c/sup\u003eLu-PSMA-Therapy in mPC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTwenty-two patients with mPC who underwent WB-DWMRI and \u003csup\u003e68\u003c/sup\u003eGa-PSMA-PET/CT within three months before \u003csup\u003e177\u003c/sup\u003eLu-PSMA therapy were identified. The PSMA SUV (mean, standard-deviation, peak) and corresponding DW Imaging (DWI) parameters (ADC mean, ADC kurtosis, volume) were extracted from five hottest lesions (highest SUVmean) on PSMA-PET/CT. Lesion response was assessed using modified PERCIST and MET-RADS-P criteria. Multilevel logistic regression and area under receiver operating characteristics (AUROC) analyses identified predictive biomarkers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003e65 bone lesions and 30 lymph nodes were analysed pre- and post-therapy. Forty-four (68%) bone and 16 (53%) lymph nodes lesions responded to treatment. SUV mean and peak were almost identical (rank-correlation\u0026thinsp;=\u0026thinsp;0.97) and had high predictive performance for response with AUROC of 0.74 (95% CI: 0.62\u0026ndash;0.86) and 0.75 (95% CI: 0.61\u0026ndash;0.88) respectively. Lesion volume showed good performance (AUROC\u0026thinsp;=\u0026thinsp;0.69, 95% CI 0.57\u0026ndash;0.82) and moderately correlated with SUVmean (rank-correlation\u0026thinsp;=\u0026thinsp;0.43). Neither ADCmean (AUROC\u0026thinsp;=\u0026thinsp;0.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.47) or kurtosis (AUROC\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;=\u0026thinsp;0.171) were predictive. On regression modelling, a 1-unit volume increase, raised response odds by 4.3 (95% CI 0.7\u0026ndash;27) in bone lesions, and 6.2 (95% CI 0.6\u0026ndash;67) in lymph nodes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06). A 1-unit SUVmean increase raised response odds by 1.5 (95% CI 1.1\u0026ndash;2.0) in bone lesions and 1.2 (95% CI 1.0\u0026ndash;1.4) in lymph nodes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). No evidence suggested combining volume with SUVmean enhanced predictive performance over SUVmean alone (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.58).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBaseline PSMA SUVmean, SUVpeak, and volume are promising predictive biomarkers for lesion-level response to \u003csup\u003e177\u003c/sup\u003eLu-PSMA-Therapy. Combining functional and structural imaging biomarkers could improve treatment stratification and response assessment. Further validation in larger studies, alongside patient-level analysis and expansion beyond the five lesions is needed to refine predictive models for clinical application.\u003c/p\u003e","manuscriptTitle":"Predictive imaging biomarkers on Whole-Body Diffusion Weighted MRI (WB-DWMRI) and 68 Ga-PSMA-PET/CT for 177 Lu-PSMA-Therapy in metastatic prostate cancer (mPC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 20:09:11","doi":"10.21203/rs.3.rs-8564323/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-21T15:17:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T13:33:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T16:18:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255305736764819656776649373392157089176","date":"2026-02-05T08:09:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186087922949976596699300121971567006034","date":"2026-02-03T18:16:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172417581734199933866278259999524910940","date":"2026-02-02T17:35:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52658128031716648575955514863951034053","date":"2026-01-29T14:06:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324315293222705745381503346681577042600","date":"2026-01-29T13:28:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T12:58:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T05:16:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T03:45:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Imaging","date":"2026-01-09T21:20:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"adde7711-8e25-4437-b29c-1eae71801143","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T11:55:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 20:09:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8564323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8564323","identity":"rs-8564323","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}