Diagnostic accuracy of a machine learning approach applied to delayed [ 18F]-Florbetaben positron emission tomography in patients with suspected light-chain cardiac amyloidosis

Diagnostic accuracy of a machine learning approach applied to delayed [ 18F]-Florbetaben positron emission tomography in patients with suspected light-chain cardiac amyloidosis | 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 Diagnostic accuracy of a machine learning approach applied to delayed [ 18 F]-Florbetaben positron emission tomography in patients with suspected light-chain cardiac amyloidosis Assuero Giorgetti, Maria Filomena Santarelli, Dario Genovesi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7968762/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2026 Read the published version in Clinical and Translational Imaging → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose The diagnosis of AL-CA is often difficult and requires invasive assessment by tissue biopsy. The purpose of the study was to evaluate the diagnostic accuracy of a machine learning approach applied to delayed [ 18 F]-florbetaben positron emission tomography (PET) uptake in identifying patients with light-chain cardiac amyloidosis (AL-CA). Methods 32 patients (age 67 ± 10 years, 9 women) with biopsy-proven diagnosis of AL-CA and 45 control subjects, referred with the initial clinical suspicion (age 74 ± 11 years, 7 women) and later diagnosed with non-AL-CA pathology, underwent a cardiac PET/computed tomography scan. Cardiac [ 18 F]-Florbetaben PET uptake was assessed using static acquisition 110 min after radiotracer injection. Results Semiquantitative radiotracer uptake showed higher SUV values in patients with AL-CA than in control subjects (p < 0.001). Machine Learning, specifically unsupervised Fuzzy C-means algorithm, proved to be an optimal methodology for classification, with sensitivity of 0.87 and specificity of 0.90 for SUV mean , and sensitivity of 0.87 and specificity of 0.90 for SUV max . These values are similar to the ones obtained by statistical analysis (sensitivity 0.87, specificity 0.90 for SUV mean and sensitivity 0.96, specificity 0.80 for SUV max ), but the cut-off determined by fuzzy C-means analysis better separates the two groups of subjects. Conclusion Machine learning analysis of delayed [ 18 F]-Florbetaben cardiac uptake may discriminate AL-CA from mimicking conditions and may represent a noninvasive tool for the diagnosis of AL-CA promising to avoid tissue biopsy for a certain diagnosis. [18F]-Florbetaben positron emission tomography/computed tomography machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2026 Read the published version in Clinical and Translational Imaging → Version 1 posted Editorial decision: Revision requested 26 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers invited by journal 31 Oct, 2025 Editor assigned by journal 31 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 28 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. 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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-7968762","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543229188,"identity":"c82517eb-d6dd-4d09-82d9-7da48630ea2b","order_by":0,"name":"Assuero 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amyloidosis\u003c/p\u003e","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":"clinical-and-translational-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cati","sideBox":"Learn more about [Clinical and Translational Imaging](http://link.springer.com/journal/40336)","snPcode":"40336","submissionUrl":"https://submission.nature.com/new-submission/40336/3","title":"Clinical and Translational Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"[18F]-Florbetaben, positron emission tomography/computed tomography, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7968762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7968762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eThe diagnosis of AL-CA is often difficult and requires invasive assessment by tissue biopsy. The purpose of the study was to evaluate the diagnostic accuracy of a machine learning approach applied to delayed [\u003csup\u003e18\u003c/sup\u003eF]-florbetaben positron emission tomography (PET) uptake in identifying patients with light-chain cardiac amyloidosis (AL-CA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e32 patients (age 67\u0026thinsp;\u0026plusmn;\u0026thinsp;10 years, 9 women) with biopsy-proven diagnosis of AL-CA and 45 control subjects, referred with the initial clinical suspicion (age 74\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years, 7 women) and later diagnosed with non-AL-CA pathology, underwent a cardiac PET/computed tomography scan. Cardiac [\u003csup\u003e18\u003c/sup\u003eF]-Florbetaben PET uptake was assessed using static acquisition 110 min after radiotracer injection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSemiquantitative radiotracer uptake showed higher SUV values in patients with AL-CA than in control subjects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Machine Learning, specifically unsupervised Fuzzy C-means algorithm, proved to be an optimal methodology for classification, with sensitivity of 0.87 and specificity of 0.90 for SUV\u003csub\u003emean\u003c/sub\u003e, and sensitivity of 0.87 and specificity of 0.90 for SUV\u003csub\u003emax\u003c/sub\u003e. These values are similar to the ones obtained by statistical analysis (sensitivity 0.87, specificity 0.90 for SUV\u003csub\u003emean\u003c/sub\u003e and sensitivity 0.96, specificity 0.80 for SUV\u003csub\u003emax\u003c/sub\u003e), but the cut-off determined by fuzzy C-means analysis better separates the two groups of subjects.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eMachine learning analysis of delayed [\u003csup\u003e18\u003c/sup\u003eF]-Florbetaben cardiac uptake may discriminate AL-CA from mimicking conditions and may represent a noninvasive tool for the diagnosis of AL-CA promising to avoid tissue biopsy for a certain diagnosis.\u003c/p\u003e","manuscriptTitle":"Diagnostic accuracy of a machine learning approach applied to delayed [ 18F]-Florbetaben positron emission tomography in patients with suspected light-chain cardiac amyloidosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 10:53:06","doi":"10.21203/rs.3.rs-7968762/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-26T16:05:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T14:15:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243798953014905433761352380346577299072","date":"2025-11-24T12:32:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T12:45:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30124293434796510255215491622346195295","date":"2025-11-08T18:04:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T10:21:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T10:21:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T18:43:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical and Translational Imaging","date":"2025-10-28T12:14:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-and-translational-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cati","sideBox":"Learn more about [Clinical and Translational Imaging](http://link.springer.com/journal/40336)","snPcode":"40336","submissionUrl":"https://submission.nature.com/new-submission/40336/3","title":"Clinical and Translational Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c8bbf9e2-489d-4b29-9eef-2b6441c495b6","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:07:22+00:00","versionOfRecord":{"articleIdentity":"rs-7968762","link":"https://doi.org/10.1007/s40336-026-00748-w","journal":{"identity":"clinical-and-translational-imaging","isVorOnly":false,"title":"Clinical and Translational Imaging"},"publishedOn":"2026-01-23 15:58:43","publishedOnDateReadable":"January 23rd, 2026"},"versionCreatedAt":"2025-11-12 10:53:06","video":"","vorDoi":"10.1007/s40336-026-00748-w","vorDoiUrl":"https://doi.org/10.1007/s40336-026-00748-w","workflowStages":[]},"version":"v1","identity":"rs-7968762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7968762","identity":"rs-7968762","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}