A neurocomputational model for prostate carcinoma detection.
A neurocomputational model for prostate carcinoma detection.
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用于前列腺癌检测的神经计算模型。
DOI：
10.1002/cncr.11748
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Niederberger,CraigS
中科院分区：
文献类型：
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作者：
Kalra,Pankaj;Togami,Joanna;BansalBS,Gaurav;Partin,AlanW;Brawer,MichaelK;Babaian,RichardJ;Ross,LawrenceS;Niederberger,CraigS
背景目前的前列腺癌筛查指南主要依赖于直肠指检（DRE）和前列腺特异性抗原（PSA）。前列腺癌的患者风险因素还包括年龄、种族、家族史和复杂的PSA。然而，由于这些变量中的每一个与前列腺癌的非线性关系，很难基于线性单变量分析来可靠地预测每个患者的风险。作者研究了一个神经网络模型的前列腺癌的风险由7个现成的临床features. METHODS数据库为目前的研究包括3268名男子最近评估前列腺癌的早期检测。评估的7个临床特征包括年龄、种族、家族史、国际前列腺症状评分（IPSS）、DRE、总PSA和复合PSA。数据集中的348例受试者包括具有确定的前列腺活检结局的男性，并且7个特征中至少有6个可用。将数据集随机分为训练集（60%）和测试集（40%），使用n1/n2交叉验证来评估模型准确性，并使用线性和二次判别函数分析和神经计算系统进行建模。在获得具有可接受的拟合优度的模型后，使用Wilks广义似然比检验进行反向回归分析以评估每个输入变量的统计显著性。在测试集中，神经计算系统的接收操作特征（ROC）面积为0.825，而总PSA和复合PSA单独的ROC面积分别为0.678和0.697。Logistic回归的ROC曲线下面积为0.510，线性判别函数为0.674，二次判别函数为0.011。所有均显著小于神经计算模型的ROC面积（allPs < 0.002）。反向回归Wilks的广义似然比检验的基础上证明每个输入功能是高度显着的模型（allPs ≤ 0.000001）。CONCLUSIONSThe作者建模的组合，以及描述患者的风险因素前列腺癌使用神经计算系统与可接受的拟合优度。他们证明了模型所基于的七个变量中的每一个对模型性能都至关重要。作者介绍了该模型用于临床，并建议临床医生在决定进行前列腺活检时使用该模型。癌症2003年。2003年美国癌症协会
BACKGROUNDCurrent guidelines for prostate carcinoma screening rely primarily on the digital rectal examination (DRE) and prostate specific antigen (PSA). Well described patient risk factors for prostate carcinoma also include age, ethnicity, family history, and complexed PSA. However, due to the nonlinear relation of each of these variables with prostate carcinoma, it is difficult to predict reliably each patient's risk based on linear univariate analysis. The authors investigated a neural network to model the risk of prostate carcinoma by seven readily available clinical features.METHODSThe database for the current study comprised 3268 men recently evaluated for the early detection of prostate carcinoma. The seven clinical features evaluated included age, race, family history, International Prostate Symptom Score (IPSS), DRE, and total and complexed PSA. Three hundred forty‐eight subjects in the dataset included men with determined prostate biopsy outcomes and for whom at least 6 of 7 features were available. The dataset was divided randomly into a training set (60%) and a test set (40%), with n1/n2 cross‐validation used to evaluate model accuracy, and was modeled with linear and quadratic discriminant function analysis and a neural computational system. After a model with acceptable goodness of fit was achieved, reverse regression analysis using Wilks's generalized likelihood ratio test was performed to evaluate the statistical significance of each input variable.RESULTSThe receiving operating characteristic (ROC) area for the neural computational system in the test set was 0.825, whereas total PSA and complexed PSA alone had ROC areas of 0.678 and 0.697, respectively. The ROC area of logistic regression in the test set was 0.510, linear discriminant function analysis was 0.674, and quadratic discriminant function analysis was 0.011. All were significantly less than the ROC area of the neural computational model (allPs < 0.002). Reverse regression based on Wilks's generalized likelihood ratio test demonstrated each input feature to be highly significant to the model (allPs ≪ 0.000001).CONCLUSIONSThe authors modeled a combination of well described patient risk factors for prostate carcinoma using a neural computational system with acceptable goodness of fit. They demonstrated that each of the seven variates on which the model was based was critically significant to model performance. The authors presented this model for clinical use and suggested that clinicians use it in deciding to perform prostate biopsy. Cancer 2003. © 2003 American Cancer Society.