Briganti Alberto, Gallina Andrea, Suardi Nazareno, Chun Felix K-H, Walz Jochen, Heuer Roman, Salonia Andrea, Haese Alexander, Perrotte Paul, Valiquette Luc, Graefen Markus, Rigatti Patrizio, Montorsi Francesco, Huland Hartwig, Karakiewicz Pierre I
Department of Urology, Vita-Salute University, Milan, Italy.
BJU Int. 2008 Mar;101(5):556-60. doi: 10.1111/j.1464-410X.2007.07321.x. Epub 2007 Nov 13.
To compare the performance and discriminant properties of two instruments (a tree-structured regression model and a logistic regression-based nomogram), recently developed to predict lymph node invasion (LNI) at radical prostatectomy (RP), in a contemporary cohort of European patients.
The cohort comprised 1525 consecutive men treated with RP and bilateral pelvic LN dissection (PLND) in two tertiary academic centres in Europe. Clinical stage, pretreatment prostate-specific antigen (PSA) level and biopsy Gleason sum were used to test the ability of the regression tree and the nomogram to predict LNI. Accuracy was quantified by the area under the receiver operating characteristic curve (AUC). All analyses were repeated for each participating institution.
The AUC for the nomogram was 81%, vs 77% for the regression tree (P = 0.007). When data were stratified according to institution, the nomogram invariably had a higher AUC than the regression tree (Hamburg cohort: nomogram 82.1% vs regression tree 77.0%, P = 0.002; Milan cohort: 82.4% vs 75.9%, respectively; P = 0.03).
Nomogram-based predictions of LNI were more accurate than those derived from a regression tree; therefore, we recommend the use of nomogram-derived predictions.
在一组当代欧洲患者中，比较最近开发的两种用于预测根治性前列腺切除术（RP）时淋巴结侵犯（LNI）的工具（一种树形回归模型和一种基于逻辑回归的列线图）的性能和判别特性。
该队列包括在欧洲两个三级学术中心接受RP和双侧盆腔淋巴结清扫术（PLND）的1525名连续男性患者。临床分期、术前前列腺特异性抗原（PSA）水平和活检Gleason评分用于测试回归树和列线图预测LNI的能力。准确性通过受试者操作特征曲线（AUC）下的面积进行量化。对每个参与机构重复所有分析。
列线图的AUC为81%，而回归树为77%（P = 0.007）。当根据机构对数据进行分层时，列线图的AUC始终高于回归树（汉堡队列：列线图82.1% vs回归树77.0%，P = 0.002；米兰队列：分别为82.4% vs 75.9%；P = 0.03）。
基于列线图的LNI预测比回归树得出的预测更准确；因此，我们建议使用基于列线图的预测。
