Elsevier

European Urology

Volume 58, Issue 4, October 2010, Pages 551-558
European Urology

Prostate Cancer
Prostate Cancer Prevention Trial and European Randomized Study of Screening for Prostate Cancer Risk Calculators: A Performance Comparison in a Contemporary Screened Cohort

https://doi.org/10.1016/j.eururo.2010.06.023Get rights and content

Abstract

Background

Several models can predict the risk of prostate cancer (PCa) on biopsy.

Objective

To evaluate the performance of the Prostate Cancer Prevention Trial (PCPT) and European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculators in detecting PCa in a contemporary screened cohort.

Design, setting, and participants

We analyzed prebiopsy characteristics of 525 consecutive screened patients submitted to biopsy, as required by the risk calculators, in one European center between 2006 and 2007.

Measurements

Comparisons were done using tests of accuracy (area under the receiver operating characteristic curve [AUC-ROC]), calibration plots, and decision curve analysis. Biopsy predictors were identified by univariate and multivariate logistic regression.

Results and limitations

PCa was detected in 35.2% of the subjects. Among predictors included in the calculators, the logarithmic transformations of prostate volume and prostate-specific antigen (PSA), digital rectal examination, previous biopsy status, and age were significantly associated with PCa; transrectal ultrasound abnormalities and family history were not. AUC-ROC for the ERSPC calculator was significantly higher than the PCPT calculator and PSA alone (80.1%, 74.4%, and 64.3%, respectively). Calibration plots showed better performance for the ERSPC calculator; nevertheless, ERSPC may underestimate risk, while PCPT tends to overestimate predictions. Decision curve analysis displayed higher net benefit for the ERSPC calculator; 9% and 23% unnecessary biopsies can be avoided if a threshold probability of 20% and 30%, respectively, is adopted. In contrast, the PCPT model displayed very limited benefit. Our findings apply to a screened European cohort submitted to extended biopsy schemes; consequently, caution should be exerted when considering different populations.

Conclusions

The ERSPC risk calculator, by incorporating several risks factors, can aid in the estimation of individual PCa risk and in the decision to perform biopsy. The ERSPC calculator outperformed the PCPT model, which is of very limited value, in a contemporary cohort of screened patients.

Introduction

Nomograms and risk calculators have shown better accuracy than prostate-specific antigen (PSA) in predicting prostate cancer (PCa) on biopsy. Nevertheless, constructed models may not apply well externally since widely varying risk levels are generated for similar patients by different models [1].

Two online risk calculators to predict individual risk of a positive biopsy have recently become available [2], [3]. The risk of positive biopsy for the Prostate Cancer Prevention Trial (PCPT) calculator depends on PSA, family history, outcome of digital rectal examination (DRE), and prior biopsy [4]. The European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator estimates the chance of positive biopsy in previously unscreened (risk indicator 3), previously screened but not biopsied (risk indicator 4), and previously screened and biopsied (risk indicator 5) men, according to PSA, ultrasound-assessed prostate volume (PV), outcome of DRE, outcome of transrectal ultrasound (TRUS), and prior biopsy status [5].

Currently, four studies have externally validated the PCPT risk calculator [6], [7], [8], [9] and only one study has externally validated the risk indicator 3 (previously unscreened) of the ERSPC calculator [10]. Risk indicators 4 and 5 have not been validated. A comparison of the PCPT and ERSPC risk calculators using virtual standard index cases pointed out differences in risk estimates between them [11], but no studies have compared their performance in a clinical setting.

In this study we analyze and compare their performance in a contemporary cohort of screened patients.

Section snippets

Materials and methods

We reviewed the records of 593 consecutive patients who underwent TRUS-guided prostate biopsy between January 2006 and December 2007 at our institution. Men with previous diagnosis of PCa or atypical small acinar proliferation of the prostate, PSA <0.5 ng/ml or >50 ng/ml, or PV <10 ml or >150 ml, were excluded (PSA and PV boundaries defined by the admitted input).

Previously screened patients (PSA judged as normal in the previous 2 yr, with or without DRE) were referred for prostate biopsy by our

Results

Among the 593 records surveyed, we identified 545 patients who met our criteria for analysis; 20 patients were excluded for missing information about family history (n = 17), and/or DRE results (n = 5). Therefore, 525 different patients were included in this analysis; their characteristics are shown in Table 1.

PCa was diagnosed in 185 patients (35.2%), 94 being high grade (Gleason ≥7). The detection rate was significantly lower on R-biopsy compared to I-biopsy (21.6% and 42.1%, respectively, p < 

Discussion

Statistical and computational models have been developed to predict more accurately an individual’s risk of harboring PCa at biopsy, mostly because PSA and PSA-related measurements have proved to be limited in this task. In a recent review including 23 studies examining 36 predictive models, 14 direct comparisons between model and PSA accuracies (AUC-ROC) showed a benefit from nomograms or artificial neural networks over PSA alone varying between 2% and 26% [1].

The original study from which the

Conclusions

The ERSPC risk calculator, by incorporating several risks factors, can aid in estimating individual risk of PCa and deciding the need for prostate biopsy in our daily practice, especially on initial biopsy, although risk could be underestimated mainly due to higher sampling schemes in current practice. In contrast, the PCPT calculator has very limited value when applied to biopsy decision-making in a screened population.

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