Ohori Makoto, Kattan Michael W, Koh Hideshige, Maru Norio, Slawin Kevin M, Shariat Shahrokh, Muramoto Masatoshi, Reuter Victor E, Wheeler Thomas M, Scardino Peter T
Department of Urology, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA.
J Urol. 2004 May;171(5):1844-9; discussion 1849. doi: 10.1097/01.ju.0000121693.05077.3d.
We developed a model to predict the side specific probability of extracapsular extension (ECE) in radical prostatectomy (RP) specimens based on the clinical features of the cancer.
We studied 763 patients with clinical stage T1c-T3 prostate cancer who were diagnosed by systematic needle biopsy and subsequently treated with RP. Candidate predictor variables associated with ECE were clinical T stage, the highest Gleason sum in any core, percent positive cores, percent cancer in the cores from each side and serum prostate specific antigen (PSA). Receiver operating characteristic (ROC) analyses were performed to assess the predictive value of each variable alone and in combination. We constructed and internally validated nomograms to predict the side specific probability of ECE based on logistic regression analysis.
Overall 30% of the patients and 17% of 1,526 prostate lobes (left or right) had ECE. The areas under the ROC curves (AUC) of the standard features in predicting side specific probability of ECE were 0.627 for PSA, 0.695 for clinical T stage on each side and 0.727 for Gleason sum on each side. When these features were combined predictive accuracy increased to 0.788. The highest value (0.806) was achieved by adding the percent positive cores and the percent cancer in the biopsy specimen to the standard features. The resulting nomograms were internally validated and had excellent calibration and discrimination accuracy.
Standard clinical features of prostate cancer in each lobe-PSA, palpable induration and biopsy Gleason sum-can be used to predict the side specific probability of ECE in RP specimens. The predictive accuracy is increased by adding information from systematic biopsy results. The predictive nomograms are sufficiently accurate for use in clinical practice in decisions such as wide versus close dissection of the cavernous nerves from the prostate.
我们基于癌症的临床特征开发了一种模型，用于预测根治性前列腺切除术（RP）标本中包膜外扩展（ECE）的侧别特异性概率。
我们研究了763例经系统穿刺活检确诊并随后接受RP治疗的临床分期为T1c - T3期前列腺癌患者。与ECE相关的候选预测变量包括临床T分期、任何一个穿刺核心中的最高Gleason评分、阳性核心百分比、每侧穿刺核心中的癌症百分比以及血清前列腺特异性抗原（PSA）。进行了受试者操作特征（ROC）分析，以评估每个变量单独及联合使用时的预测价值。我们基于逻辑回归分析构建并进行了内部验证列线图，以预测ECE的侧别特异性概率。
总体而言，30%的患者以及1526个前列腺叶（左侧或右侧）中的17%出现了ECE。在预测ECE侧别特异性概率时，标准特征的ROC曲线下面积（AUC）分别为：PSA为0.627，每侧临床T分期为0.695，每侧Gleason评分总和为0.727。当这些特征联合使用时，预测准确性提高到了0.788。通过将阳性核心百分比和活检标本中的癌症百分比添加到标准特征中，获得了最高值（0.806）。所得列线图经过内部验证，具有良好的校准和区分准确性。
每个前列腺叶中前列腺癌的标准临床特征——PSA、可触及硬结和活检Gleason评分总和——可用于预测RP标本中ECE的侧别特异性概率。通过添加系统活检结果的信息可提高预测准确性。预测列线图对于临床实践中诸如前列腺海绵体神经广泛与近距离解剖等决策而言，准确性足够高。
