Development and External Validation of a Prediction Model to Identify Candidates for Prostate Biopsy

Vinayak G. Wagaskar, Anna Lantz, Stanislaw Sobotka, Parita Ratnani, Sneha Parekh, Ugo Giovanni Falagario, Li Li, Sara Lewis, Kenneth Haines, Sanoj Punnen, Peter Wiklund, Ash Tewari

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Purpose: Prostate biopsies are associated with infectious complications and approximately 80% are either benign or clinically insignificant prostate cancer. Our aim is to develop and independently validate prediction model to avoid unnecessary prostate biopsies by predicting clinically significant prostate cancer (csPCa) Materials and Methods: Retrospective analysis of single-center cohort (Mount Sinai Hospital, NY) of 1632 men who underwent systematic or combined systematic and Magnetic Resonance Imaging (MRI)/ultrasound fusion targeted prostate biopsy between 2014-2020. External cohort (University of Miami) included 622 men that underwent biopsy. Outcome for predicting csPCa was defined as International Society of Urologic Pathology (ISUP) Gleason grade ≥ 2 on biopsy. Multivariable logistic regression analysis was performed to build nomogram using coefficients of logit function. Nomogram validation was performed in external cohort by plotting receiver operating characteristics (ROC). We also plotted decision curve analysis (DCA) and compared nomogram-predicted probabilities with actual rates of csPCa probabilities in external cohort. Results: Of 1632 men, 43% showed csPCa on biopsy. PSA density, prior negative biopsy, and Prostate Imaging and Reporting Data System (PI-RADS) scores 3, 4, and 5 were significant predictors for csPCa. ROC for prediction of csPCa was 0.88 in external cohort. There was agreement between predicted and actual rate of csPCa in external cohort. DCA demonstrated net benefit using the model. Using the prediction model at threshold of 30, 35% of biopsies and 46% of diagnosed indolent PCa could be avoided, while missing 5% of csPCa. Conclusion: Using our prediction model can help reduce unnecessary prostate biopsies with minimal impact on csPCa detection rates.

Original languageEnglish
JournalUrology Journal
Volume19
Issue number5
DOIs
StatePublished - 2022

Keywords

  • Biopsy
  • Logistic models
  • Magnetic resonance imaging
  • Nomograms
  • Prostate cancer

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