Key result
New multivariable model incorporating PHI achieves an AUC of ~0.75 for detecting aggressive prostate cancer.
Why the study?
The clinical utility of the Prostate Health Index as a continuous variable in multivariable risk assessment for aggressive prostate cancer was not established in a large multicentre US population.
Does the Prostate Health Index improve multivariable risk prediction of aggressive prostate cancer in men with PSA 2-10 ng/mL?
Cohort (n=728)
Yes
Does the Prostate Health Index improve multivariable risk prediction of aggressive prostate cancer in men with PSA 2-10 ng/mL?
Effect estimate: AUC 0.746
Incorporating the Prostate Health Index into multivariable risk assessment improves the detection of aggressive prostate cancer and may reduce unnecessary biopsies.
PHI model may refine aggressive prostate cancer prediction; hypothesis-generating and should not yet change biopsy decisions.
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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Loeb et al. (2016) conducted a cohort in Prostate cancer (n=728). Prostate Health Index (PHI) vs. Standard clinical variables and currently available risk calculators (PCPT and ERSPC) was evaluated on Aggressive prostate cancer, defined as biopsy Gleason score ≥7 (AUC 0.746). A new multivariable predictive model incorporating the Prostate Health Index (PHI) achieved an area under the curve of 0.746 for detecting aggressive prostate cancer.
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