4.6 Article

Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer

期刊

BJU INTERNATIONAL
卷 120, 期 1, 页码 61-68

出版社

WILEY
DOI: 10.1111/bju.13676

关键词

Prostate Health Index; prostate cancer; risk assessment; nomogram; prostate biopsy

资金

  1. Beckman Coulter Incorporated, Chaska, MN, USA
  2. Laura and Isaac Perlmutter Cancer Center at New York University
  3. Louis Feil Charitable Lead Trust
  4. National Institutes of Health/National Cancer Institute (NIH/NCI) Johns Hopkins Prostate SPORE Grant [P50CA58236]
  5. Early Detection Research Network NIH/NCI Grant [U01-CA86323]
  6. NIH/NCI [U01 CA86323, U24 CA115102, U01CA113913]
  7. Urological Research Foundation
  8. Northwestern-University of Chicago-NorthShore University Prostate SPORE grant [NIH/NCI P50 CA90386-05S2]
  9. Robert H. Lurie Comprehensive Cancer Center grant [NIH/NCI P30 CA60553]
  10. Beckman Coulter Incorporated

向作者/读者索取更多资源

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.

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