Istituto Nazionale Tumori, Fondazione G. Pascale, Napoli, Italy.
Eur Urol. 2011 Jan;59(1):81-7. doi: 10.1016/j.eururo.2010.09.036. Epub 2010 Oct 14.
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.
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.
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.
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.
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.
前列腺癌抗原 3（PCA3）在诊断前列腺癌（PCa）方面具有一定的潜力，但目前尚未就其临床应用达成共识。多变量预测模型显示，其准确性高于个体危险因素。
比较两种现有的风险评估工具在检测 PSA<10ng/ml 的“灰色区域”PCa 中的表现：更新的前列腺癌预防试验（PCPT）计算器和 Chun 列线图。
设计、设置和参与者：2008 年 10 月至 2009 年 10 月期间，意大利多中心前瞻性研究纳入了 218 例因 PSA 异常（不包括 PSA>10ng/ml 的患者）和/或直肠指检异常就诊的患者。所有患者均接受了≥12 针前列腺活检。
使用 Progensa 检测试剂盒（Gen-Probe，圣地亚哥，加利福尼亚州，美国）评估 PCA3 评分。通过准确性测试（接受者操作特征曲线下面积[AUC-ROC]）、校准图和决策曲线分析比较两种模型。使用单变量和多变量逻辑回归确定活检预测因子。此外，通过 AUC-ROC 和预测值分析 PCA3 的性能。
73 例患者（33.5%）检测出 PCa。在纳入模型的预测因子中，只有 PCA3、PSA 和前列腺体积具有显著的预测价值。与 Chun 列线图相比，更新的 PCPT 计算器的 AUC-ROC 更高（79.6% vs 71.5%；p=0.043）；然而，Chun 列线图的整体校准效果更好，在决策曲线分析中具有更高的净收益。使用 25%的概率阈值，不会遗漏高等级癌症；PCPT 计算器将节省 11%的活检，不会遗漏癌症，而 Chun 列线图将节省 22%的可避免活检，但会遗漏 4.1%的非高等级癌症。患者数量较少可能导致个体变量或模型比较的预测价值缺乏统计学意义。
Chun 列线图和 PCPT 计算器都可以通过纳入 PCA3 来帮助确定活检的决策，具体方法是确定个体患 PCa 的风险，特别是在 PSA 水平<10ng/ml 时。
