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A clinically significant prostate cancer predictive model using digital rectal examination prostate volume category to stratify initial prostate cancer suspicion and reduce magnetic resonance imaging demand

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dc.contributor.author Morote, Juan
dc.contributor.author Borque-Fernando, Ángel
dc.contributor.author Triquell, Marina
dc.contributor.author Campistol, Miriam
dc.contributor.author Celma, Anna
dc.contributor.author Regis, Lucas
dc.contributor.author Abascal-Junquera, José María
dc.contributor.author Servian, Pol
dc.contributor.author Planas, Jacques
dc.contributor.author Mendez Fernández, Olga
dc.contributor.author Esteban, Luis M.
dc.contributor.author Trilla, Enrique
dc.date.accessioned 2023-03-01T07:29:29Z
dc.date.available 2023-03-01T07:29:29Z
dc.date.issued 2022
dc.identifier.citation Morote J, Borque-Fernando Á, Triquell M, Campistol M, Celma A, Regis L, et al. A clinically significant prostate cancer predictive model using digital rectal examination prostate volume category to stratify initial prostate cancer suspicion and reduce magnetic resonance imaging demand. Cancers (Basel). 2022 Oct 18; 14(20): 5100. DOI: 10.3390/cancers14205100
dc.identifier.issn 2072-6694
dc.identifier.uri http://hdl.handle.net/10230/55983
dc.description.abstract A predictive model including age, PCa family history, biopsy status (initial vs repeat), DRE (normal vs abnormal), serum prostate-specific antigen (PSA), and DRE prostate volume ca-tegory was developed to stratify initial PCa suspicion in 1486 men with PSA > 3 ng/mL and/or abnormal DRE, in whom mpMRI followed; 2- to 4-core TRUS-guided biopsies where Prostate Imaging Report and Data System (PI-RADS) > 3 lesions and/or 12-core TRUS systematic biopsies were performed in one academic institution between 1 January 2016-31 December 2019. The csPCa detection rate, defined as International Society of Uro-Pathology grade group 2 or higher, was 36.9%. An external validation of designed BCN-RC 1 was carried out on 946 men from two other institutions in the same metropolitan area, using the same criteria of PCa suspicion and diagnostic approach, yielded a csPCa detection rate of 40.8%. The areas under the receiver operating characteristic curves of BCN-RC 1 were 0.823 (95% CI: 0.800-0.846) in the development cohort and 0.837 (95% CI: 0.811-0.863) in the validation cohort (p = 0.447). In both cohorts, BCN-RC 1 exhibited net benefit over performing mpMRI in all men from 8 and 12% risk thresholds, respectively. At 0.95 sensitivity of csPCa, the specificities of BCN-RC 1 were 0.24 (95% CI: 0.22-0.26) in the development cohort and 0.34 (95% CI: 0.31-0.37) in the validation cohort (p < 0.001). The percentages of avoided mpMRI scans were 17.2% in the development cohort and 22.3% in the validation cohort, missing between 1.8% and 2% of csPCa among men at risk of PCa. In summary, BCN-RC 1 can stratify initial PCa suspicion, reducing the demand of mpMRI, with an acceptable loss of csPCa.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI
dc.rights Copyright © 2022 by Morote J, Borque-Fernando Á, Triquell M, Campistol M, Celma A, Regis L, et al. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title A clinically significant prostate cancer predictive model using digital rectal examination prostate volume category to stratify initial prostate cancer suspicion and reduce magnetic resonance imaging demand
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3390/cancers14205100
dc.subject.keyword Clinically significant
dc.subject.keyword Development
dc.subject.keyword External validation
dc.subject.keyword Magnetic resonance imaging
dc.subject.keyword Predictive model
dc.subject.keyword Prostate cancer
dc.subject.keyword Risk calculator
dc.subject.keyword Suspicion
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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