Evidence map›Paper›PMID 31875279›Full record

ArticleInternational urology and nephrology2020

Optimal threshold of the prostate health index in predicting aggressive prostate cancer using predefined cost-benefit ratios and prevalence.

Miroslav Stojadinovic, Ivan Vukovic, Milos Ivanovic, Milorad Stojadinovic, Dragan Milovanovic, Damnjan Pantic, Slobodan Jankovic

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Article in International urology and nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Miroslav StojadinovicDepartment of Urology, Clinic of Urology and Nephrology, Clinical Centre "Kragujevac", Zmaj Jovina 30, 34 000, Kragujevac, Serbia. midinac@gmail.com.ORCID http://orcid.org/0000-0002-5215-8729
Ivan VukovicClinic of Urology, Clinical Center of Serbia, University of Belgrade, Belgrade, Serbia.
Milos IvanovicDepartment of Mathematics and Informatics, Faculty of Science, University of Kragujevac, Kragujevac, Serbia.
Milorad StojadinovicFaculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia.
Dragan MilovanovicPharmacology and Toxicology Department, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia.
Damnjan PanticDepartment of Urology, Clinic of Urology and Nephrology, Clinical Centre "Kragujevac", Zmaj Jovina 30, 34 000, Kragujevac, Serbia.
Slobodan JankovicPharmacology and Toxicology Department, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia.ORCID http://orcid.org/0000-0002-1519-8828

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposesThe aim of the study was to determine optimal threshold of the Prostate Health Index (Phi) for predicting aggressive prostate cancer (PCa), taking into account misclassification costs, prevalence, and plausible risk factors.

methodsThis prospective cohort study analyzed patients undergoing prostate biopsy and Phi testing. The primary endpoint was aggressive PCa, defined as biopsy Gleason score ≥ 7. The data about age, total prostate-specific antigen (PSA), percentage of free PSA (%fPSA), and digital rectal examination (DRE) were extracted from the patient files. We divided the patients to the low- and high-risk group. The clinical usefulness of the Phi was assessed by the decision curve analysis. The predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), per-class metrics, and the potential reduction in unnecessary biopsies. The uncertain interval of Phi values was also determined.

resultsThere were 200 men included in the study, 35 (17.5%) of them having aggressive PCa. Important predictors of aggressive PCa were %fPSA, DRE, Phi, and belonging to the high-risk group. With optimal threshold of 30.7, about 32% unnecessary biopsies would be avoided. The optimal threshold of Phi was lower in the high-risk group than in the low-risk group. The AUC for detection of aggressive PCa was 0.791. Per-class metrics showed that the Phi has insufficient diagnostic accuracy. The lower and upper limits of the uncertain interval were 41.8 and 51.4, respectively.

conclusionDifferent thresholds of the Phi could be optimal, depending on prevalence, patient characteristics, and misclassification costs. Further studies with a larger patient sample are necessary to confirm our conclusions.

Indexed as

AgedCost-Benefit AnalysisHumansKallikreinsMaleMiddle AgedPredictive Value of TestsPrevalencePrognosisProspective StudiesProstate-Specific AntigenProstatic NeoplasmsProtein PrecursorsRisk Factors(-2)pro-prostate-specific antigen, humanKallikreinsKLK3 protein, humanProstate-Specific AntigenProtein PrecursorsDecision analysisOptimal thresholdProstate biopsyProstate cancerProstate health indexUncertain interval

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.