An algorithm for predicting nonorgan confined prostate cancer using the results obtained from sextant core biopsies with prostate specific antigen level
An algorithm for predicting nonorgan confined prostate cancer using the results obtained from sextant core biopsies with prostate specific antigen level
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DOI：
10.1016/s0022-5347(01)65590-3
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发表时间：
1996-10-01
影响因子：
6.6
通讯作者：
Veltri, RW
中科院分区：
文献类型：
--
作者：
Badalament, RA;Miller, MC;Veltri, RW
目的：结合血清前列腺特异性抗原(PSA)检测前列腺癌患者的血清前列腺特异性抗原(PSA)水平，探讨其对非器官受限前列腺癌的预测能力。材料与方法：采用六分切活检标本及术前PSA图像分析指标。研究人群包括210名病理分期的疾病患者(192名PSA患者)。结果：单因素Logistic回归分析显示，定量核分级、术前前列腺特异性抗原、肿瘤受累总百分比、阳性核心数、术前Gleason评分、受累程度大于5%的基底和/或心尖活检对疾病器官受阻状态的预测有统计学意义(P<0.006)。对包括脱氧核糖核酸倍体在内的这些单变量进行向后逐步Logistic回归，以计算预测疾病器官受限状态的多变量模型。该算法的敏感度为85.7%，特异度为71.3%，阳性预测值为72.9%，阴性预测值为84.7%，受试者工作特征曲线下面积为85.9%。结论：与以往报道的方法相比，该算法可以结合前列腺活检病理研究、术前PSA血检和一种新的图像分析变量--定量核分级，建立一个更准确预测非器官受限前列腺癌的多变量算法。
Purpose: We determined the enhanced ability to predict nonorgan confined prostate cancer using several histopathological and quantitative nuclear imaging parameters combined with serum prostate specific antigen (PSA).Materials and Methods: Several independent pathological and quantitative image analysis variables obtained from sextant biopsy specimens, as well as preoperative PSA were used. The study population included 210 patients with pathologically staged disease (192 with PSA). All variables were examined by univariate and multivariate logistic regression analyses to assess ability to predict disease organ confinement status.Results: Univariate logistic regression analysis demonstrated that, in decreasing order, quantitative nuclear grade, preoperative PSA, total percent tumor involvement, number of positive sextant cores, preoperative Gleason score and involvement of more than 5% of a base and/or apex biopsy were significant (p less than or equal to 0.006) for prediction of disease organ confinement status. Backward stepwise logistic regression was applied to these univariately significant variables, including deoxyribonucleic acid ploidy, to calculate a multivariate model for prediction of disease organ confinement status. This algorithm had a sensitivity of 85.7%, specificity 71.3%, positive predictive value 72.9%, negative predictive value 84.7% and area under the receiver operating characteristic curve 85.9%.Conclusions: Information from pathological study of sextant prostate biopsies, preoperative PSA blood test and a new image analysis variable termed quantitative nuclear grade can be combined to create a multivariate algorithm that can predict more accurately nonorgan confined prostate cancer compared to previously reported methods.