A prostate cancer risk calculator: Use of clinical and magnetic resonance imaging data to predict biopsy outcome in North American men

Authors

  • Adam Kinnaird University of Alberta
  • Wayne Brisbane
  • Lorna Kwan
  • Alan Priester
  • Ryan Chuang
  • Danielle E. Barsa
  • Merdie Delfin
  • Anthony Sisk
  • Daniel Margolis
  • Ely Felker
  • Jim Hu
  • Leonard S. Marks

DOI:

https://doi.org/10.5489/cuaj.7380

Keywords:

MRI-guided biopsy, Risk calculator, nomogram, PCRC-MRI

Abstract

Introduction: A functional tool to optimize patient selection for magnetic resonance imaging (MRI)-guided prostate biopsy (MRGB) is an unmet clinical need. We sought to develop a prostate cancer risk calculator (PCRC-MRI) that combines MRI and clinical characteristics to aid decision-making for MRGB in North American men.

Methods: Two prospective registries containing 2354 consecutive men undergoing MRGB (September 2009 to April 2019) were analyzed. Patients were randomized into five groups, with one group randomly assigned to be the validation cohort against the other four groups as the discovery cohort. The primary outcome was detection of clinically significant prostate cancer (csPCa) defined as Gleason grade group ≥2. Variables included age, ethnicity, digital rectal exam (DRE), prior biopsy, prostate-specific antigen (PSA), prostate volume, PSA density, and MRI score. Odds ratios (OR) were calculated from multivariate logistic regression comparing two models: one with clinical variables only (clinical) against a second combining clinical variables with MRI data (clinical+MRI).

Results: csPCa was present in 942 (40%) of the 2354 men available for study. The positive and negative predictive values for csPCa in the clinical+MRI model were 57% and 89%, respectively. The area under the curve of the clinical+MRI model was superior to the clinical model in discovery (0.843 vs. 0.707, p<0.0001) and validation (0.888 vs. 0.757, p<0.0001) cohorts. Use of PCRC-MRI would have avoided approximately 16 unnecessary biopsies in every 100 men. Of all variables examined, Asian ethnicity was the most protective factor (OR 0.46, 0.29–0.75) while MRI score 5 indicated greatest risk (OR15.8, 10.5–23.9).

Conclusions: A risk calculator (PCRC-MRI), based on a large North American cohort, is shown to improve patient selection for MRGB, especially in preventing unnecessary biopsies. This tool is available at https://www.uclahealth.org/urology/prostate-cancer-risk-calculator and may help rationalize biopsy decision-making.

Downloads

Download data is not yet available.

Downloads

Published

2021-10-18

How to Cite

Kinnaird, A., Brisbane, W., Kwan, L., Priester, A., Chuang, R., Barsa, D. E., Delfin, M., Sisk, A., Margolis, D., Felker, E., Hu, J., & Marks, L. S. (2021). A prostate cancer risk calculator: Use of clinical and magnetic resonance imaging data to predict biopsy outcome in North American men. Canadian Urological Association Journal, 16(3), E161–6. https://doi.org/10.5489/cuaj.7380

Issue

Vol. 16 No. 3 (2022): CUAJ March

Section

Original Research

License

You, the Author(s), assign your copyright in and to the Article to the Canadian Urological Association. This means that you may not, without the prior written permission of the CUA:

  • Post the Article on any Web site
  • Translate or authorize a translation of the Article
  • Copy or otherwise reproduce the Article, in any format, beyond what is permitted under Canadian copyright law, or authorize others to do so
  • Copy or otherwise reproduce portions of the Article, including tables and figures, beyond what is permitted under Canadian copyright law, or authorize others to do so.

The CUA encourages use for non-commercial educational purposes and will not unreasonably deny any such permission request.

You retain your moral rights in and to the Article. This means that the CUA may not assert its copyright in such a way that would negatively reflect on your reputation or your right to be associated with the Article.

The CUA also requires you to warrant the following:

  • That you are the Author(s) and sole owner(s), that the Article is original and unpublished and that you have not previously assigned copyright or granted a licence to any other third party;
  • That all individuals who have made a substantive contribution to the article are acknowledged;
  • That the Article does not infringe any proprietary right of any third party and that you have received the permissions necessary to include the work of others in the Article; and
  • That the Article does not libel or violate the privacy rights of any third party.