Elsevier

European Urology Focus

Volume 1, Issue 2, September 2015, Pages 185-190
European Urology Focus

Prostate Cancer
Comparison of Two Prostate Cancer Risk Calculators that Include the Prostate Health Index

https://doi.org/10.1016/j.euf.2015.06.004Get rights and content

Abstract

Background

Risk prediction models for prostate cancer (PCa) have become important tools in reducing unnecessary prostate biopsies. The Prostate Health Index (PHI) may increase the predictive accuracy of such models.

Objectives

To compare two PCa risk calculators (RCs) that include PHI.

Design, setting, and participants

We evaluated the predictive performance of a previously developed PHI-based nomogram and updated versions of the European Randomized Study of Screening for Prostate Cancer (ERSPC) RCs based on digital rectal examination (DRE): RC3 (no prior biopsy) and RC4 (prior biopsy). For the ERSPC updates, the original RCs were recalibrated and PHI was added as a predictor. The PHI-updated ERSPC RCs were compared with the Lughezzani nomogram in 1185 men from four European sites. Outcomes were biopsy-detectable PC and potentially advanced or aggressive PCa, defined as clinical stage >T2b and/or a Gleason score ≥7 (clinically relevant PCa).

Results and limitations

The PHI-updated ERSPC models had a combined area under the curve for the receiver operating characteristic (AUC) of 0.72 for all PCa and 0.68 for clinically relevant PCa. For the Lughezzani PHI-based nomogram, AUCs were 0.75 for all PCa and 0.69 for clinically relevant PCa. For men without a prior biopsy, PHI-updated RC3 resulted in AUCs of 0.73 for PCa and 0.66 for clinically relevant PCa. Decision curves confirmed these patterns, although the number of clinically relevant cancers was low.

Conclusion

Differences between RCs that include PHI are small. Addition of PHI to an RC leads to further reductions in the rate of unnecessary biopsies when compared to a strategy based on prostate-specific antigen measurement.

Patient summary

Risk prediction models for prostate cancer have become important tools in reducing unnecessary prostate biopsies. We compared two risk prediction models for prostate cancer that include the Prostate Health Index. We found that these models are equivalent to each other, and both perform better than the prostate-specific antigen test alone in predicting cancer.

Introduction

Prostate cancer (PCa) is the most common form of cancer in men in Europe [1]. Prostate-specific antigen (PSA) testing is the mainstay of early PCa detection [2]. However, PSA has limited specificity in predicting the presence of PCa, which leads to unnecessary biopsies and the diagnosis of potentially indolent PCa [3], [4]. A prostate biopsy is an invasive procedure, and apart from costs and anxiety, is not without a risk of complications [5].

PSA-based multivariable prediction tools have been developed to improve the prediction of biopsy-detectable PCa. Well-known and externally validated models include the European Randomized Study of Prostate Cancer (ERSPC) risk calculators (RCs) (http://www.prostatecancer-riskcalculator.com/) [6], the Prostate Cancer Prevention Trial calculator (http://deb.uthscsa.edu/URORiskCalc/Pages/calcs.jsp) [7], and the Montreal model [8]. Risk prediction models have become an important tool in reducing unnecessary prostate biopsies [9]. The addition of new biomarkers to an existing prediction tool may increase its accuracy. Novel and promising markers in the field of PCa include the Prostate Health Index (PHI), based on data for total PSA (tPSA), free PSA (fPSA), and [–2]proPSA (p2PSA). PHI has been approved for use by the US Food and Drug Administration (http://www.accessdata.fda.gov/cdrh_docs/pdf9/p090026a.pdf).

Lughezzani et al [10] developed and validated a nomogram that includes PHI. We aimed to compare PCa RCs that include PHI, the Lughezzani PHI-based nomogram, and PHI-updated digital rectal examination (DRE)-based ERSPC models.

Section snippets

Participants

Our study cohort comprised 1185 men from four sites in Europe (Paris, Rennes, Hamburg, and Münster). Data on tPSA, fPSA, p2PSA, PHI, DRE, prostate volume, and biopsy outcome (PCa detected yes/no) were collected for all men. Participants in the study underwent a biopsy according to the standard clinical practice routinely used at each participating site, which was a ≥10-core biopsy. We calculated PHI using the equation (p2PSA / fPSA) × √tPSA [11]. tPSA was between 2.0 and 10.0 ng/ml (Access

Participants

Among the 1185 men studied, 797 (67%, 453 with PCa) had no previous biopsy and 388 (170 with PCa) had a previous negative biopsy (Table 1). Median PSA was 5.0 ng/ml for men with no prior biopsy and 5.6 ng/ml for men with a prior biopsy, and median PHI values were 47 and 41, respectively. Men without a prior biopsy were more likely to have (clinically relevant) cancer compared to men with a prior biopsy.

Updating the ERSPC model

For total PCa, PHI improved discrimination (AUC 0.72, 95% confidence interval [CI] 0.69–0.75)

Discussion

PHI and its PSA components add important diagnostic information in distinguishing PCa from normal prostate tissue and when considered in addition to existing PCa RC models. However, the net reduction in biopsies was limited and only observed at PCa risk thresholds of approximately 20–30% using the two RCs we investigated. Nevertheless, it must be noted that NB would be higher in a population in which PCa prevalence is closer to the relevant decision threshold (eg, ∼30%), as found for some

Conclusions

In conclusion, PHI increases the predictive ability of previously developed RCs for detection of cancer. However, only limited reductions in the rates of unnecessary biopsies are possible for both the Lughezzani and the updated ERSPC models.

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  • Cited by (0)

    These authors contributed equally to this work.

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