Evaluating the Quality of Local Programs for Early Detection of Significant Prostate Cancer

Juan Morote Robles, Nahuel Paesano, Natàlia Picola, Jesús Muñoz-Rodriguez, Xavier Ruiz-Plazas, Marta V. Muñoz-Rivero, Ana Celma, Gemma García-de Manuel, Jose Maria Abascal, Pol Servian

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Quality control of programs for detection of significant prostate cancer (sPCa) could be defined by the correlation between observed and reference 95% confidence intervals (CIs) for Prostate Imaging-Reporting and Data System (PI-RADS) categories. We used the area under the receiver operating characteristic curve (AUC) for the Barcelona magnetic resonance imaging (MRI) predictive model to screen the quality of ten participant centers in the sPCa opportunistic early detection program in Catalonia. We set an AUC of <0.8 as the criterion for suboptimal quality. Quality was confirmed in terms of the correlation between actual sPCa detection rates and reference 95% CIs. For a cohort of 2624 men with prostate-specific antigen >3.0 ng/ml and/or a suspicious digital rectal examination who underwent multiparametric MRI and two- to four-core targeted biopsies of PI-RADS ≥3 lesions and/or 12-core systematic biopsy, AUC values ranged from 0.527 to 0.914 and were <0.8 in four centers (40%). There was concordance between actual sPCa detection rates and reference 95% CIs for one or two PI-RADS categories when the AUC was <0.8, and for three or four PI-RADS categories when the AUC was ≥0.8. A review of procedures used for sPCa detection should be recommended in centers with suboptimal quality. Patient summary: We tested a method for assessing quality control for centers carrying out screening for early detection of prostate cancer. We found that the method can identify centers that may need to review their procedures for detection of significant prostate cancer.

Original languageEnglish
Pages (from-to)5-8
Number of pages4
JournalEuropean Urology Open Science
Volume66
DOIs
Publication statusPublished - Aug 2024

Keywords

  • Clinically significant prostate cancer
  • Early detection
  • Quality control
  • Predictive models

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