Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer1. (22nd November 2016)

• Record Type: Journal Article
• Authors: Loeb, Stacy; Shin, Sanghyuk S.; Broyles, Dennis L.; Wei, John T.; Sanda, Martin; Klee, George; Partin, Alan W.; Sokoll, Lori; Chan, Daniel W.; Bangma, Chris H.; van Schaik, Ron H.N.; Slawin, Kevin M.; Marks, Leonard S.; Catalona, William J.
• Is Part Of: BJU international. Volume 120:Number 1(2017)
• Page Start: 61
• Page End: 68
• Publication Date: 2016-11-22
• Subjects: Prostate Health Index -- prostate cancer -- risk assessment -- nomogram -- prostate biopsy; Genitourinary organs -- Diseases -- Periodicals; Genitourinary organs -- Surgery -- Periodicals; Urology -- Periodicals; 616.6
• DOI: 10.1111/bju.13676
• Languages: English
• ISSNs: 1464-4096

Abstract

Abstract : Objective: To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. Materials and Methods: The study population included 728 men, with prostate‐specific antigen (PSA) levels of 2–10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi‐site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI‐based multivariable predictive model, and created a nomogram. Results: Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap‐corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision‐curve analysis. Conclusion: Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis.