Validation of the prostate health index in a predictive model of prostate cancer
Authors
Sanchís Bonet, Ángeles; Barrionuevo González, Marta; Bajo Chueca, Ana María; Pulido Fonseca, Lina Alexandra; Ortega Polledo, Luis Enrique; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60033DOI: 10.1016/j.acuro.2017.06.003
ISSN: 0210-4806
Date
2018Affiliation
Universidad de Alcalá. Departamento de Cirugía, Ciencias Médicas y Sociales; Universidad de Alcalá. Departamento de Biología de SistemasFunders
Foundation for Biomedical Research of the Príncipe de Asturias University Hospital.
Bibliographic citation
Actas Urologicas Espanolas, 2018, v. 42, n. 1, p. 25-32
Keywords
Prostate cancer
Prostate health index
Predictive models
Decision curve analysis
Prostate biopsy
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/aceptedVersion
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© Elsevier
Access rights
info:eu-repo/semantics/openAccess
Abstract
Objectives: To validate and analyse the clinical usefulness of a predictive model of prostate cancer that incorporates the biomarker ?[-2] pro prostate-specific antigen? using the prostate health index (PHI) in decision making for performing prostate biopsies. Material and methods: We isolated serum from 197 men with an indication for prostate biopsy to determine the total prostate-specific antigen (tPSA), the free PSA fraction (fPSA) and the [-2] proPSA (p2PSA). The PHI was calculated as p2PSA/fPSA×?tPSA. We created 2 predictive models that incorporated clinical variables along with tPSA or PHI. The performance of PHI was assessed with a discriminant analysis using receiver operating characteristic curves, internal calibration and decision curves. Results: The areas under the curve for the tPSA and PHI models were 0.71 and 0.85, respectively. The PHI model showed a better ability to discriminate and better calibration for predicting prostate cancer but not for predicting a Gleason score in the biopsy ?7. The decision curves showed a greater net benefit with the PHI model for diagnosing prostate cancer when the probability threshold was 15-35% and greater savings (20%) in the number of biopsies. Conclusions: The incorporation of p2PSA through PHI in predictive models of prostate cancer improves the accuracy of the risk stratification and helps in the decision-making process for performing prostate biopsies.
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