Cancer Prognostics and Health Outcomes Unit, University of Montréal Health Centre, Montreal, Canada.
Urology. 2011 Dec;78(6):1363-7. doi: 10.1016/j.urology.2011.07.1423.
A formal validation and head-to-head comparison of the National Comprehensive Cancer Network (NCCN) practice guideline lymph node invasion (LNI) nomogram, Partin tables, and D'Amico risk-classification was conducted for prediction of LNI at radical prostatectomy (RP).
We focused on 20,877 patients treated with RP and pelvic lymph node dissection (PLND) between 2004 and 2006 within the Surveillance, Epidemiology and End Results database. The discrimination of the 3 tools in predicting histologically confirmed LNI was quantified using the area under the curve (AUC). Calibration plots were used to graphically depict the performance characteristics of the examined tools. In addition, we relied on decision curve analyses to compare the 3 models directly in a head-to-head fashion.
Overall, 2.5% of patients had LNI. The NCCN LNI nomogram (AUC 82%) outperformed the Partin tables (73%) and the D'Amico risk-classification (75%) for prediction of LNI. Calibration plots revealed that all 3 tools overestimated the risk of LNI. Partin tables showed the highest net-benefit for probability threshold range between 1% and 4%. Conversely, the NCCN LNI nomogram showed the highest net-benefit for the remaining threshold probabilities.
The NCCN LNI nomogram had the highest discrimination accuracy. However, using the decision curve analysis, the Partin tables demonstrated the highest net benefit when a threshold probability of LNI is <4%. In contrast, the NCCN LNI nomogram had the highest net benefit when the threshold probability used to perform PLND is greater than 4%.
对国家综合癌症网络（NCCN）实践指南淋巴结侵犯（LNI）列线图、Partin 表和 D'Amico 风险分类进行正式验证和头对头比较，以预测根治性前列腺切除术（RP）中的 LNI。
我们专注于 2004 年至 2006 年间 Surveillance, Epidemiology and End Results 数据库中接受 RP 和盆腔淋巴结清扫术（PLND）治疗的 20877 例患者。使用曲线下面积（AUC）量化 3 种工具在预测组织学证实的 LNI 方面的区分能力。校准图用于图形化描绘所检查工具的性能特征。此外，我们依赖决策曲线分析直接对头对头比较 3 种模型。
总体而言，2.5%的患者有 LNI。NCCN LNI 列线图（AUC 为 82%）在预测 LNI 方面优于 Partin 表（73%）和 D'Amico 风险分类（75%）。校准图显示所有 3 种工具都高估了 LNI 的风险。Partin 表在概率阈值范围为 1%至 4%之间显示出最高的净收益。相反，NCCN LNI 列线图在其余阈值概率下显示出最高的净收益。
NCCN LNI 列线图具有最高的区分准确性。然而，使用决策曲线分析，当 LNI 的阈值概率<4%时，Partin 表显示出最高的净收益。相比之下，当用于执行 PLND 的阈值概率大于 4%时，NCCN LNI 列线图具有最高的净收益。
