Elsevier

European Urology

Volume 59, Issue 1, January 2011, Pages 81-87
European Urology

Prostate Cancer
Prostate Cancer Detection in the “Grey Area” of Prostate-Specific Antigen Below 10 ng/ml: Head-to-Head Comparison of the Updated PCPT Calculator and Chun’s Nomogram, Two Risk Estimators Incorporating Prostate Cancer Antigen 3

https://doi.org/10.1016/j.eururo.2010.09.036Get rights and content

Abstract

Background

Prostate cancer antigen 3 (PCA3) holds promise in diagnosing prostate cancer (PCa), but no consensus has been reached on its clinical use. Multivariable predictive models have shown increased accuracy over individual risk factors.

Objective

To compare the performance of the two available risk estimators incorporating PCA3 in the detection of PCa in the “grey area” of prostate-specific antigen (PSA) <10 ng/ml: the updated Prostate Cancer Prevention Trial (PCPT) calculator and Chun’s nomogram.

Design, setting, and participants

Two hundred eighteen patients presenting with an abnormal PSA (excluding those with PSA >10 ng/ml) and/or abnormal digital rectal examination were prospectively enrolled in a multicentre Italian study between October 2008 and October 2009. All patients underwent ≥12-core prostate biopsy.

Measurements

PCA3 scores were assessed using the Progensa assay (Gen-Probe, San Diego, CA, USA). Comparisons between the two models were performed using tests of accuracy (area under the receiver operating characteristic curve [AUC-ROC]), calibration plots, and decision curve analysis. Biopsy predictors were identified by univariable and multivariable logistic regression. In addition, performance of PCA3 was analysed through AUC-ROC and predictive values.

Results and limitations

PCa was detected in 73 patients (33.5%). Among predictors included in the models, only PCA3, PSA, and prostate volume retained significant predictive value. AUC-ROC was higher for the updated PCPT calculator compared to Chun’s nomogram (79.6% vs 71.5%; p = 0.043); however, Chun’s nomogram displayed better overall calibration and a higher net benefit on decision curve analysis. Using a probability threshold of 25%, no high-grade cancers would be missed; the PCPT calculator would save 11% of biopsies, missing no cancer, whereas Chun’s nomogram would save 22% of avoidable biopsies, although missing 4.1% non–high-grade cancers. The small number of patients may account for the lack of statistical significance in the predictive value of individual variables or model comparison.

Conclusions

Both Chun’s nomogram and the PCPT calculator, by incorporating PCA3, can assist in the decision to biopsy by assignment of an individual risk of PCa, specifically in the PSA levels <10 ng/ml.

Introduction

Nomograms and risk calculators have shown better accuracy than prostate-specific antigen (PSA) in predicting prostate cancer (PCa) on biopsy [1]. The use of prostate cancer antigen 3 (PCA3) testing in clinical practice still remains to be established; its indications and cut-off values have not so far been agreed on. Its clinical usefulness may be greater in the so-called grey area of PSA values or in the setting of repeat biopsy (R-biopsy). It has also been suggested that the PCA3 test should be incorporated into diagnostic models that include multiple variables [2].

To our knowledge, only two multivariable risk estimators that include PCA3 have been published. The Prostate Cancer Prevention Trial (PCPT) calculator was updated to include PCA3 in risk calculation [3], and Chun et al have recently published a nomogram that includes a PCA3 score to estimate risk of cancer on biopsy [4]. The former is available online [5] and comprises six other variables beside PCA3: race, age, PSA, family history, digital rectal examination (DRE), and prior biopsy. The latter was built as a nomogram that includes PCA3 together with five other variables: age, PSA, DRE, prior biopsy, and prostate volume. Both have been externally validated in European cohorts [3], [6]. In this study, a head-to-head comparison of these two models is performed in a clinical scenario of patients in the grey area of PSA (ie, ≤10 ng/ml) referred to biopsy.

Section snippets

Study design

This was a prospective, multicentre Italian study. Men referred for prostate biopsy because of abnormal PSA and/or suspicious DRE were enrolled in three Italian centres (I.N.T. “G. Pascale” Foundation, Napoli; Magna Graecia University, Catanzaro; Second University of Naples, Napoli) between October 2008 and October 2009. The respective independent ethics committees approved the study protocol, and informed consent was obtained. Men with a PSA level >10 ng/ml, who received medical therapy in the

Results

Two hundred eighteen patients were enrolled and submitted to prostate biopsy. Their characteristics are shown in Table 1. Prostate biopsy was positive for cancer in 73 patients (33.5%); 27 high-grade cancers (Gleason ≥7) were diagnosed.

Total PSA and PCA3 scores were significantly higher on positive biopsy patients. In contrast, fPSA% was significantly lower in cancer patients. Increasing PCA3 scores (according to previously published cut-offs) were accompanied by increasing rates of positive

Discussion

To more accurately predict an individual’s risk of harbouring PCa at biopsy, statistical and computational models were developed, mostly because PSA and PSA-related measurements were shown to be limited in this task. Schröder and Kattan performed a systematic review of 36 predictive models, including 14 direct comparisons between model and PSA accuracies (AUC-ROC), showing a benefit from nomograms or artificial neural networks over PSA varying between 2% and 26% [1].

Recently, Cavadas et al

Conclusions

Both Chun’s nomogram and the PCPT calculator, by incorporating PCA3 into their multivariable predictive models, can assist in the decision to biopsy by assigning an individual risk of harbouring PCa, specifically in the PSA levels <10 ng/ml, where dilemmas are most frequent.

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1

These authors contributed equally to this article.

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