Improving multivariable prostate cancer risk assessment using the Prostate Health Index - Abstract

OBJECTIVES: To analyse the clinical utility of a prediction model incorporating both clinical information as well as a novel biomarker in order to inform the decision for prostate biopsy in an Irish cohort.

PATIENTS AND METHODS: Serum isolated from 250 men from three tertiary referral centres with pre-biopsy blood draws was analysed for total PSA, free PSA and p2PSA. From this, the phi score was calculated (phi=(p2PSA/fPSA)*√tPSA). Their clinical information was used to derive their risk according to the Prostate Cancer Prevention Trial risk model (PCPT). Two clinical prediction models were created via multivariable regression consisting of age, family history, abnormality on digital rectal exam, prior negative biopsy and either PSA or phi score respectively. Calibration plots, receiver-operating characteristic (ROC) curves as well as decision curves were generated to assess the performance of the three models.

RESULTS: The PSA model and phi model were both highly calibrated in this cohort, with the phi model demonstrating the best correlation between predicted probabilities and actual outcome. The areas under the ROC curve for the phi model, PSA model and PCPT were 0.77, 0.71 & 0.69 respectively for the prediction of PCa and 0.79, 0.72 & 0.72 for the prediction of high grade PCa. Decision curve analysis demonstrated a superior net benefit of the phi model over both the PSA model and PCPT in the diagnosis of PCa and high grade PCa over the entire range of risk probabilities.

CONCLUSION: A logical and standardised approach to the use of clinical risk factors can allow for more accurate risk stratification of men under investigation for PCa. The measurement of p2PSA and the integration of this biomarker into a clinical prediction model can further increase the accuracy of risk stratification, helping to better inform the decision for prostate biopsy in a referral population.

Written by:
Foley RW, Gorman L, Sharifi N, Murphy K, Moore H, Tuzova AV, Perry AS, Murphy TB, Lundon DJ, Watson RW.   Are you the author?
Conway Institute of Biomolecular and Biomedical Research, University College Dublin; UCD School of Medicine and Medical Science, University College Dublin.

Reference: BJU Int. 2015 Apr 3. Epub ahead of print.
doi: 10.1111/bju.13143

PubMed Abstract
PMID: 25847734 Prostate Cancer Section