Predicting the presence and side of extracapsular extension: A nomogram for staging prostate cancer
Predicting the presence and side of extracapsular extension: A nomogram for staging prostate cancer
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DOI：
10.1097/01.ju.0000121693.05077.3d
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发表时间：
2004-05-01
影响因子：
6.6
通讯作者：
Scardino, PT
中科院分区：
文献类型：
--
作者：
Ohori, M;Kattan, MW;Scardino, PT
目的：我们开发了一个模型，以预测侧特定的概率囊外扩展（ECE）在根治性前列腺切除术（RP）标本的临床特点的cancer.Materials和方法：我们研究了763例临床分期T1 C-T3前列腺癌谁被诊断为系统性穿刺活检，随后与RP治疗。与ECE相关的候选预测变量是临床T分期、任何核心中的最高Gleason总和、阳性核心百分比、每侧核心中的癌症百分比和血清前列腺特异性抗原（PSA）。进行受试者工作特征（ROC）分析，以评估每个变量单独和组合的预测值。我们构建和内部验证的列线图，以预测特定的概率ECE的Logistic回归analysis.Results的基础上，总的30%的患者和17%的1,526前列腺叶（左或右）有ECE。标准特征预测ECE侧别概率的ROC曲线下面积（AUC）分别为PSA 0.627、临床T分期0.695和Gleason总和0.727。当这些特征组合时，预测准确度增加到0.788。通过将活检样本中的阳性核心百分比和癌症百分比添加到标准特征中来实现最高值（0.806）。由此产生的列线图进行了内部验证，并有很好的校准和歧视accuracy.Conclusions：标准的前列腺癌的临床特征，在每个叶PSA，可触及的硬结和活检Gleason总和，可以用来预测侧特定的概率ECE在RP标本。通过添加来自系统活检结果的信息来提高预测准确性。预测列线图足够准确，可用于临床实践中的决策，如广泛与密切的海绵体神经从前列腺的解剖。
Purpose: 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.Materials and Methods: 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.Results: 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.Conclusions: 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.