Steuber Thomas, Graefen Markus, Haese Alexander, Erbersdobler Andreas, Chun Felix K-H, Schlom Thorsten, Perrotte Paul, Huland Hartwig, Karakiewicz Pierre I
Department of Urology, Hamburg, Germany.
J Urol. 2006 Mar;175(3 Pt 1):939-44; discussion 944. doi: 10.1016/S0022-5347(05)00342-3.
We have previously have reported a tree structured regression model for predicting SS-ECE. Others recently reported a logistic regression based SS-ECE nomogram. We developed a nomogram and compared the performance and discriminant properties of the tree regression and the nomogram in a contemporary cohort of European patients treated with radical retropubic prostatectomy.
The cohort consisted of 1,118 patients with pretreatment prostate specific antigen 0.1 to 73.2 ng/ml (median 6.6). Each of the 2,236 prostate lobes was considered separately. Clinical stage, pretreatment PSA, biopsy Gleason sum, percent positive cores and percent cancer in the biopsy specimen were used as predictors in a logistic regression model predicting SS-ECE. Regression coefficients were then used to generate an SS-ECE nomogram. Performance characteristics and discriminant properties of the previously published tree regression were also tested in the same cohort. For internal validation and to decrease overfit bias 200 bootstrap re-samples were applied to accuracy estimates for each method.
ECE was present in 303 of 1,118 radical retropubic prostatectomy specimens (27%) and in 385 lobes (17%). In logistic regression models all variables were statistically significant multivariate predictors of SS-ECE except the percent of positive biopsy cores (p = 0.7). Bootstrap corrected predictive accuracy of the SS-ECE nomogram was 0.840 vs 0.700 for the tree regression model.
Logistic regression based nomogram predictions of SS-ECE are highly accurate and represent a valuable aid for assessing the risk of ECE prior to surgery.
我们之前报道了一种用于预测精囊外扩展（SS-ECE）的树状结构回归模型。最近其他人报道了一种基于逻辑回归的SS-ECE列线图。我们开发了一种列线图，并在接受根治性耻骨后前列腺切除术的当代欧洲患者队列中比较了树状回归和列线图的性能及判别特性。
该队列由1118例患者组成，其术前前列腺特异性抗原为0.1至73.2 ng/ml（中位数为6.6）。对2236个前列腺叶分别进行分析。临床分期、术前前列腺特异性抗原（PSA）、活检Gleason评分总和、阳性核心百分比以及活检标本中的癌症百分比被用作逻辑回归模型中预测SS-ECE的预测因子。然后使用回归系数生成SS-ECE列线图。在同一队列中还测试了之前发表的树状回归的性能特征和判别特性。为了进行内部验证并减少过度拟合偏差，对每种方法的准确性估计应用了200次自举重采样。
在1118例根治性耻骨后前列腺切除标本中，303例（27%）存在精囊外扩展，在385个叶中存在（17%）。在逻辑回归模型中，除了活检阳性核心百分比外（p = 0.7），所有变量都是SS-ECE的统计学显著多变量预测因子。SS-ECE列线图的自举校正预测准确性为0.840，而树状回归模型为0.700。
基于逻辑回归的SS-ECE列线图预测高度准确，是术前评估精囊外扩展风险的有价值辅助工具。
