Division of Urology, Department of Surgery, Kaohsiung Veterans General Hospital, 386 Ta-Chung 1st Rd., Zuoying, Kaohsiung, Taiwan.
School of Medicine, National Yang-Ming University, Taipei, Taiwan.
World J Urol. 2021 Mar;39(3):797-802. doi: 10.1007/s00345-020-03256-2. Epub 2020 May 20.
To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps.
1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented.
Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong's method: P < 0.001) and high-grade PCa (AUC: 0.869 vs 0.873 vs 0.767, P < 0.001). Using a ≥ 10% risk threshold for high-grade PCa to biopsy, our model could save 67.2% of total biopsies; among these saved biopsies, only 3.4% high-grade PCa would be missed.
Our new logistic regression model, similar to the Rotterdam app, outperformed the Coral app in the prediction of PCa and high-grade PCa. Additionally, our model could save unnecessary biopsies and avoid missing clinically significant PCa in the Taiwanese population.
开发一种新的台湾前列腺癌（PCa）风险模型，以预测 PCa，并将其预测性能与两种成熟的 PCa 风险计算器应用程序进行比较。
回顾性分析了 2012 年至 2019 年间在台湾一家三级医疗中心接受前列腺活检的 1545 名男性患者。建立了一个五折交叉验证逻辑回归风险模型，以计算 PCa 和高级别 PCa（Gleason 评分≧7）的概率，并与 Rotterdam 和 Coral 应用程序进行比较。使用接受者操作特征曲线下的面积（AUC）分析区分度。通过拟合优度检验图形评估校准。进行决策曲线分析以评估临床效用。在不同的活检风险阈值下，展示了活检减少量与低级别和高级别 PCa 漏诊量的比例。
总体而言，1309 名患者中有 278 名（21.2%）被诊断为 PCa，278 名患者中有 181 名（65.1%）患有高级别 PCa。我们的模型和 Rotterdam 应用程序在检测 PCa 方面均优于 Coral 应用程序（AUC：0.795 比 0.792 比 0.697，DeLong 方法：P<0.001）和高级别 PCa（AUC：0.869 比 0.873 比 0.767，P<0.001）。使用高级别 PCa 活检的风险阈值≥10%，我们的模型可以减少 67.2%的总活检量；在这些减少的活检中，只有 3.4%的高级别 PCa 会被漏诊。
我们的新逻辑回归模型与 Rotterdam 应用程序类似，在预测 PCa 和高级别 PCa 方面优于 Coral 应用程序。此外，我们的模型可以减少不必要的活检，并避免在台湾人群中漏诊有临床意义的 PCa。
