Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada.
Eur Urol. 2012 Oct;62(4):590-6. doi: 10.1016/j.eururo.2012.04.022. Epub 2012 May 2.
Statistical prediction tools are increasingly common, but there is considerable disagreement about how they should be evaluated. Three tools--Partin tables, the European Society for Urological Oncology (ESUO) criteria, and the Gallina nomogram--have been proposed for the prediction of seminal vesicle invasion (SVI) in patients with clinically localized prostate cancer who are candidates for a radical prostatectomy.
Using different statistical methods, we aimed to determine which of these tools should be used to predict SVI.
DESIGN, SETTINGS, AND PARTICIPANTS: The independent validation cohort consisted of 2584 patients treated surgically for clinically localized prostate cancer at four North American tertiary care centers between 2002 and 2007.
Robot-assisted laparoscopic radical prostatectomy.
Primary outcome was the presence of SVI. Traditional (area under the receiver operating characteristic [ROC] curve, calibration plots, the Brier score, sensitivity and specificity, positive and negative predictive value) and novel (decision curve analysis and predictiveness curves) statistical methods quantified the predictive abilities of the three models.
Traditional statistical methods (ie, ROC plots and Brier scores) could not clearly determine which one of the three SVI prediction tools should be preferred. For example, ROC plots and Brier scores seemed biased against the binary decision tool (ESUO criteria) and gave discordant results for the continuous predictions of the Partin tables and the Gallina nomogram. The results of the calibration plots were discordant with those of the ROC plots. Conversely, the decision curve indicated that the Partin tables represent the best strategy for stratifying the risk of SVI, resulting in the highest net benefit within the whole range of threshold probabilities.
When predicting SVI, surgeons should prefer the Partin tables over the ESUO criteria and the Gallina nomogram because this tool provided the highest net benefit. In contrast to traditional statistical methods, decision curve analysis gave an unambiguous result applicable to both continuous and binary models, providing an insight into clinical utility.
统计预测工具越来越普遍，但如何评估它们存在很大分歧。有三个工具——Partin 表、欧洲泌尿外科学会（ESUO）标准和 Gallina 列线图，被提出用于预测临床局限性前列腺癌患者接受根治性前列腺切除术的精囊侵犯（SVI）。
使用不同的统计方法，我们旨在确定应该使用这些工具中的哪一个来预测 SVI。
设计、设置和参与者：独立验证队列包括 2002 年至 2007 年间在北美四家三级护理中心接受手术治疗的 2584 例临床局限性前列腺癌患者。
机器人辅助腹腔镜根治性前列腺切除术。
主要结局是 SVI 的存在。传统（接受者操作特征曲线下面积、校准图、Brier 评分、敏感性和特异性、阳性和阴性预测值）和新（决策曲线分析和预测曲线）统计方法量化了三个模型的预测能力。
传统统计方法（即 ROC 图和 Brier 评分）无法明确确定应该首选这三种 SVI 预测工具中的哪一种。例如，ROC 图和 Brier 评分似乎对二元决策工具（ESUO 标准）有偏差，并对 Partin 表和 Gallina 列线图的连续预测产生不一致的结果。校准图的结果与 ROC 图不一致。相反，决策曲线表明，Partin 表代表了分层 SVI 风险的最佳策略，在整个阈值概率范围内产生了最高的净收益。
在预测 SVI 时，外科医生应首选 Partin 表而不是 ESUO 标准和 Gallina 列线图，因为该工具提供了最高的净收益。与传统统计方法相比，决策曲线分析给出了一个适用于连续和二进制模型的明确结果，提供了对临床实用性的深入了解。
