Department of Urology, Tokyo Medical University, Tokyo, Japan.
Int J Urol. 2010 Mar;17(3):267-72. doi: 10.1111/j.1442-2042.2010.02452.x. Epub 2010 Feb 3.
To present a nomogram predicting the side-specific probability of extracapsular extension (ECE) in radical prostatectomy (RP) specimens.
Three hundred and fifty-four patients with T1c-T3a prostate cancer undergoing RP were included in the analysis. A receiver operating characteristic (ROC) analysis was carried out to evaluate the predictive values of each clinical and pathological factor, separately and in combination. Based on logistic regression analysis, a nomogram predicting the side-specific probability of ECE was developed.
Overall, 146 (40%) of 354 patients and 165 (23%) of 708 lobes had ECE pathologically. The areas under the ROC curve (AUC) of the standard features, such as serum PSA, clinical stage and biopsy Gleason sum on each side, in predicting side-specific probability of ECE were 0.624, 0.627, and 0.747, respectively. When these three features were combined, AUC increased to 0.773 which was not significantly different from 0.791 of maximum percent of cancer alone (P = 0.613) and significantly enhanced by including maximum percent of cancer on each side, 0.799 (P = 0.022). The resulting nomogram was internally validated and had excellent calibration.
The accuracy in predicting ECE is increased by combining standard clinical factors (clinical stage, serum PSA, highest Gleason score) and biopsy features, such as maximum percent of cancer in the cores. The developed nomogram is helpful when deciding whether or not neurovascular bundles can be preserved.
提出一种列线图,预测根治性前列腺切除术(RP)标本中囊外扩展(ECE)的侧特异性概率。
纳入 354 例 T1c-T3a 前列腺癌患者进行 RP 分析。进行了接受者操作特征(ROC)分析,以评估每个临床和病理因素的预测值,分别和联合。基于逻辑回归分析,建立了一种预测 ECE 侧特异性概率的列线图。
总体而言,354 例患者中有 146 例(40%)和 708 个叶中有 165 例(23%)存在 ECE 病理。标准特征(如血清 PSA、临床分期和活检 Gleason 总和)在预测 ECE 侧特异性概率方面的 ROC 曲线下面积(AUC)分别为 0.624、0.627 和 0.747。当将这三个特征结合在一起时,AUC 增加到 0.773,与单独最大癌百分比的 0.791 无显著差异(P=0.613),并且通过包括每侧最大癌百分比,AUC 显著提高,为 0.799(P=0.022)。所得列线图进行了内部验证,具有良好的校准度。
通过结合标准临床因素(临床分期、血清 PSA、最高 Gleason 评分)和活检特征(如核心中最大癌百分比),可以提高预测 ECE 的准确性。所开发的列线图有助于决定是否可以保留神经血管束。