Foley Robert W, Gorman Laura, Sharifi Neda, Murphy Keefe, Moore Helen, Tuzova Alexandra V, Perry Antoinette S, Murphy T Brendan, Lundon Dara J, Watson R William G
Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.
UCD School of Medicine and Medical Science, University College Dublin, Dublin, Ireland.
BJU Int. 2016 Mar;117(3):409-17. doi: 10.1111/bju.13143. Epub 2015 May 24.
To analyse the clinical utility of a prediction model incorporating both clinical information and a novel biomarker, p2PSA, in order to inform the decision for prostate biopsy in an Irish cohort of men referred for prostate cancer assessment.
Serum isolated from 250 men from three tertiary referral centres with pre-biopsy blood draws was analysed for total prostate-specific antigen (PSA), free PSA (fPSA) and p2PSA. From this, the Prostate Health Index (PHI) score was calculated (PHI = (p2PSA/fPSA)*√tPSA). The men's clinical information was used to derive their risk according to the Prostate Cancer Prevention Trial (PCPT) risk model. Two clinical prediction models were created via multivariable regression consisting of age, family history, abnormality on digital rectal examination, previous negative biopsy and either PSA or PHI score, respectively. Calibration plots, receiver-operating characteristic (ROC) curves and decision curves were generated to assess the performance of the three models.
The PSA model and PHI model were both well calibrated in this cohort, with the PHI model showing the best correlation between predicted probabilities and actual outcome. The areas under the ROC curve for the PHI model, PSA model and PCPT model were 0.77, 0.71 and 0.69, respectively, for the prediction of prostate cancer (PCa) and 0.79, 0.72 and 0.72, respectively, for the prediction of high grade PCa. Decision-curve analysis showed a superior net benefit of the PHI model over both the PSA model and the PCPT risk model in the diagnosis of PCa and high grade PCa over the entire range of risk probabilities.
A logical and standardized approach to the use of clinical risk factors can allow 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.
分析一种结合临床信息和新型生物标志物p2PSA的预测模型的临床效用，以便为爱尔兰一组因前列腺癌评估而转诊的男性患者的前列腺活检决策提供依据。
对来自三个三级转诊中心的250名男性患者在活检前采集的血液中分离出的血清进行总前列腺特异性抗原（PSA）、游离PSA（fPSA）和p2PSA分析。据此计算前列腺健康指数（PHI）评分（PHI = (p2PSA/fPSA)*√tPSA）。根据男性的临床信息，按照前列腺癌预防试验（PCPT）风险模型得出其风险。通过多变量回归分别创建了两个临床预测模型，一个由年龄、家族史、直肠指检异常、既往活检阴性和PSA组成，另一个由年龄、家族史、直肠指检异常、既往活检阴性和PHI评分组成。生成校准图、受试者操作特征（ROC）曲线和决策曲线以评估这三个模型的性能。
PSA模型和PHI模型在该队列中校准良好，PHI模型在预测概率与实际结果之间显示出最佳相关性。对于前列腺癌（PCa）的预测，PHI模型、PSA模型和PCPT模型的ROC曲线下面积分别为0.77、0.71和0.69；对于高级别PCa的预测，分别为0.79、0.72和0.72。决策曲线分析显示，在整个风险概率范围内，PHI模型在诊断PCa和高级别PCa方面比PSA模型和PCPT风险模型具有更高的净效益。
采用合理且标准化的方法使用临床风险因素能够对接受前列腺癌调查的男性进行更准确的风险分层。p2PSA的检测以及将该生物标志物整合到临床预测模型中可进一步提高风险分层的准确性，有助于更好地为转诊人群的前列腺活检决策提供依据。
