Shen P, Zhao J, Sun G, Chen N, Zhang X, Gui H, Yang Y, Liu J, Shu K, Wang Z, Zeng H
Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.
Andrology. 2017 May;5(3):548-555. doi: 10.1111/andr.12322.
The aim of this study was to develop nomograms for predicting prostate cancer and its zonal location using prostate-specific antigen density, prostate volume, and their zone-adjusted derivatives. A total of 928 consecutive patients with prostate-specific antigen (PSA) less than 20.0 ng/mL, who underwent transrectal ultrasound-guided transperineal 12-core prostate biopsy at West China Hospital between 2011 and 2014, were retrospectively enrolled. The patients were randomly split into training cohort (70%, n = 650) and validation cohort (30%, n = 278). Predicting models and the associated nomograms were built using the training cohort, while the validations of the models were conducted using the validation cohort. Univariate and multivariate logistic regression was performed. Then, new nomograms were generated based on multivariate regression coefficients. The discrimination power and calibration of these nomograms were validated using the area under the ROC curve (AUC) and the calibration curve. The potential clinical effects of these models were also tested using decision curve analysis. In total, 285 (30.7%) patients were diagnosed with prostate cancer. Among them, 131 (14.1%) and 269 (29.0%) had transition zone prostate cancer and peripheral zone prostate cancer. Each of zone-adjusted derivatives-based nomogram had an AUC more than 0.75. All nomograms had higher calibration and much better net benefit than the scenarios in predicting patients with or without different zones prostate cancer. Prostate-specific antigen density, prostate volume, and their zone-adjusted derivatives have important roles in detecting prostate cancer and its zonal location for patients with PSA 2.5-20.0 ng/mL. To the best of our knowledge, this is the first nomogram using these parameters to predict outcomes of 12-core prostate biopsy. These instruments can help clinicians to increase the accuracy of prostate cancer screening and to avoid unnecessary prostate biopsy.
本研究的目的是利用前列腺特异性抗原密度、前列腺体积及其区域调整衍生物开发预测前列腺癌及其区域位置的列线图。回顾性纳入了2011年至2014年期间在华西医院接受经直肠超声引导经会阴12针前列腺穿刺活检的928例连续前列腺特异性抗原（PSA）低于20.0 ng/mL的患者。将患者随机分为训练队列（70%，n = 650）和验证队列（30%，n = 278）。使用训练队列建立预测模型和相关列线图，同时使用验证队列对模型进行验证。进行单因素和多因素逻辑回归。然后，根据多因素回归系数生成新的列线图。使用ROC曲线下面积（AUC）和校准曲线验证这些列线图的辨别力和校准。还使用决策曲线分析测试了这些模型的潜在临床效果。共有285例（30.7%）患者被诊断为前列腺癌。其中，131例（14.1%）和269例（29.0%）分别患有移行区前列腺癌和外周区前列腺癌。基于每个区域调整衍生物的列线图的AUC均大于0.75。在预测不同区域前列腺癌患者方面，所有列线图的校准度更高，净效益更好。对于PSA为2.5 - 20.0 ng/mL的患者，前列腺特异性抗原密度、前列腺体积及其区域调整衍生物在检测前列腺癌及其区域位置方面具有重要作用。据我们所知，这是首个使用这些参数预测12针前列腺穿刺活检结果的列线图。这些工具可帮助临床医生提高前列腺癌筛查的准确性，并避免不必要的前列腺穿刺活检。
