Improving glomerular filtration rate measurement accuracy in renal tumor patients: a novel deep learning approach

Improving glomerular filtration rate measurement accuracy in renal tumor patients: a novel deep learning approach | 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 Improving glomerular filtration rate measurement accuracy in renal tumor patients: a novel deep learning approach Chunxing Wu, Guangfeng Chen, Qing Liao, Feng Zhang, Liangjun Xie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8994741/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Gates' Glomerular Filtration Rate (GFR) measurement via SPECT dynamic renal scintigraphy is common in clinical practices. However, its accuracy is limited by the variability of renal depth (RD) estimated by the conventional height- and weight-based Tonnesen formula, particularly in patients with renal tumors. This study compared a novel deep learning-based standalone CT RD estimation and GFR measurement method against the Tonnesen formula between renal tumor and non-tumor groups, using the double plasma sample method (DPSM) as the reference standard. Methods A retrospective analysis was conducted on 99 patients from January to December 2024, categorized into renal tumor group (n = 23) and non-tumor group (n = 76). All patients underwent 99m Tc-DTPA SPECT/CT dynamic renal scintigraphy, standalone abdominal CT, serum creatinine (Scr) testing, and DPSM. Five GFR measurement methods were compared and analyzed : (1) Gates' method with deep learning-based standalone CT segmentation and RD estimation, (2) Gates' method using Tonnesen's RD via Siemens software, (3) Gates' method using Tonnesen's RD via MMIS software, (4) Scr-based estimation, and (5) DPSM serving as the gold standard. Paired t-tests were conducted statistically to compare various GFRs between tumor and non-tumor groups. Pearson correlation coefficients were used to evaluate relationships among the various GFR methods. Results Standalone CT-derived RDs were greater than Tonnesen formula estimation. Particularly in the tumor group, CT-derived vs. Tonnesen RDs were 7.30 ± 1.51 cm vs. 5.97 ± 1.07 cm (left), and 7.15 ± 1.27 cm vs. 6.01 ± 1.08 cm (right). The mean total GFR measurements using the gold standard DPSM, CT-derived RD, Tonnesen's RD via Siemens and MMIS software, and Scr-based, were 87.91 ± 19.53, 94.61 ± 28.11, 77.29 ± 17.93, 79.28 ± 23.98, and 84.04 ± 26.17 mL/min for the tumor group, 77.56 ± 11.04, 81.30 ± 30.67, 68.07 ± 22.86, 68.22 ± 24.16, and 94.52 ± 44.78 mL/min for the non-tumor group. The CT-based measurements showed the strongest correlation with DPSM (r = 0.915 for the tumor group; r = 0.825 for the non-tumor group). Conversely, Tonnesen-based GFRs were generally lower than the reference standard DPSM, demonstrating a statistically significant difference. Conclusions Deep learning-based kidney segmentation and RD estimation using standalone abdominal CT images significantly improved the accuracy of Gates' GFR calculations. By leveraging existing diagnostic CT imaging, this method provided high clinical value – especially for patients with renal space-occupying lesions – while eliminating additional radiation exposure and reducing variability in clinical workflow. Glomerular Filtration Rate Renal Depth Renal Tumor Dynamic Renal Scintigraphy SPECT Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers invited by journal 05 May, 2026 Editor invited by journal 04 May, 2026 Editor assigned by journal 04 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 28 Feb, 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. 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approach","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":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Glomerular Filtration Rate, Renal Depth, Renal Tumor, Dynamic Renal Scintigraphy, SPECT, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-8994741/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8994741/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGates' Glomerular Filtration Rate (GFR) measurement via SPECT dynamic renal scintigraphy is common in clinical practices. However, its accuracy is limited by the variability of renal depth (RD) estimated by the conventional height- and weight-based Tonnesen formula, particularly in patients with renal tumors. This study compared a novel deep learning-based standalone CT RD estimation and GFR measurement method against the Tonnesen formula between renal tumor and non-tumor groups, using the double plasma sample method (DPSM) as the reference standard.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on 99 patients from January to December 2024, categorized into renal tumor group (n\u0026thinsp;=\u0026thinsp;23) and non-tumor group (n\u0026thinsp;=\u0026thinsp;76). All patients underwent \u003csup\u003e99m\u003c/sup\u003eTc-DTPA SPECT/CT dynamic renal scintigraphy, standalone abdominal CT, serum creatinine (Scr) testing, and DPSM. Five GFR measurement methods were compared and analyzed : (1) Gates' method with deep learning-based standalone CT segmentation and RD estimation, (2) Gates' method using Tonnesen's RD via Siemens software, (3) Gates' method using Tonnesen's RD via MMIS software, (4) Scr-based estimation, and (5) DPSM serving as the gold standard. Paired t-tests were conducted statistically to compare various GFRs between tumor and non-tumor groups. Pearson correlation coefficients were used to evaluate relationships among the various GFR methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eStandalone CT-derived RDs were greater than Tonnesen formula estimation. Particularly in the tumor group, CT-derived vs. Tonnesen RDs were 7.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51 cm vs. 5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07 cm (left), and 7.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27 cm vs. 6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08 cm (right). The mean total GFR measurements using the gold standard DPSM, CT-derived RD, Tonnesen's RD via Siemens and MMIS software, and Scr-based, were 87.91\u0026thinsp;\u0026plusmn;\u0026thinsp;19.53, 94.61\u0026thinsp;\u0026plusmn;\u0026thinsp;28.11, 77.29\u0026thinsp;\u0026plusmn;\u0026thinsp;17.93, 79.28\u0026thinsp;\u0026plusmn;\u0026thinsp;23.98, and 84.04\u0026thinsp;\u0026plusmn;\u0026thinsp;26.17 mL/min for the tumor group, 77.56\u0026thinsp;\u0026plusmn;\u0026thinsp;11.04, 81.30\u0026thinsp;\u0026plusmn;\u0026thinsp;30.67, 68.07\u0026thinsp;\u0026plusmn;\u0026thinsp;22.86, 68.22\u0026thinsp;\u0026plusmn;\u0026thinsp;24.16, and 94.52\u0026thinsp;\u0026plusmn;\u0026thinsp;44.78 mL/min for the non-tumor group. The CT-based measurements showed the strongest correlation with DPSM (r\u0026thinsp;=\u0026thinsp;0.915 for the tumor group; r\u0026thinsp;=\u0026thinsp;0.825 for the non-tumor group). Conversely, Tonnesen-based GFRs were generally lower than the reference standard DPSM, demonstrating a statistically significant difference.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDeep learning-based kidney segmentation and RD estimation using standalone abdominal CT images significantly improved the accuracy of Gates' GFR calculations. By leveraging existing diagnostic CT imaging, this method provided high clinical value \u0026ndash; especially for patients with renal space-occupying lesions \u0026ndash; while eliminating additional radiation exposure and reducing variability in clinical workflow.\u003c/p\u003e","manuscriptTitle":"Improving glomerular filtration rate measurement accuracy in renal tumor patients: a novel deep learning approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 02:52:12","doi":"10.21203/rs.3.rs-8994741/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-08T13:58:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123328667098346720597020955922145784964","date":"2026-05-08T13:10:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T14:48:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T11:28:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T11:56:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T11:54:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-02-28T11:02:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b41c5a7e-7fc0-4cc1-90f0-d7847f952de9","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-08T13:58:46+00:00","index":33,"fulltext":""},{"type":"reviewerAgreed","content":"123328667098346720597020955922145784964","date":"2026-05-08T13:10:03+00:00","index":32,"fulltext":""},{"type":"reviewersInvited","content":"12","date":"2026-05-05T14:48:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T11:28:07+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T02:52:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 02:52:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8994741","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8994741","identity":"rs-8994741","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}