Neural Network Prediction of Prostate Cancer Progression

描述(由申请人提供)：这项应用的广泛、长期目标是通过对肿瘤进展的准确诊断和准确评估来有效治疗前列腺癌。这个项目要检验的假设是，人工神经网络(ANN)可以准确地预测前列腺癌的进展。这项研究的意义和与健康相关的是，前列腺癌进展的准确预测对于识别手术非常有效的器官受限前列腺癌患者和手术效果较差但会带来不必要并发症风险的较晚期前列腺癌患者非常重要，这些患者更适合接受放射、激素和其他治疗。以前对人工神经网络的调查往往依赖于高度精选的人工神经网络，这些人工神经网络既不能证明也不能反驳神经网络的有效性。这一应用最终将决定ANN在预测前列腺癌进展方面是否比多元线性回归更准确。适当的统计模型对于临床结合一系列生物标志物和癌症预测指标的结果是很重要的。具体目标是： (1)建立了一种基于人工神经网络的病理分期预测方法，并与临床上公认的多元线性回归方法Partin Norgraph进行了比较。 (2)开发一种新的方法，使人工神经网络对前列腺癌进展的预测增加95%的可信区间。 (3)建立一种基于人工神经网络的、基于术前系列PSA检测的病理分期预测模型。 研究设计是开发基于神经网络的预测模型，并将它们与临床接受的标准和之前发表的被认为有希望但临床应用有限的结果进行比较。所使用的方法包括临床数据库的收集、人工神经网络和多元线性回归模型的分析、接收者工作特性(ROC)分析、统计估计和可信区间的计算。

DESCRIPTION (provided by applicant): The broad, long-term objective of this application is to treat prostate cancer effectively through accurate diagnosis and accurate assessment of tumor progression. The hypothesis to be tested in this project is that artificial neural networks (ANNs) can accurately predict prostate cancer progression. The significance and health-relatedness of this research is that accurate prediction of prostate cancer progression is important to identify patients with organ-confined prostate cancer for whom surgery is highly effective, and patients with more advanced prostate cancer for whom surgery is less effective but imposes unnecessary risks of complications who are more appropriate to receive radiation, hormonal, and other therapies. Previous investigation of ANNs often rely on highly-selected ANNs that do not prove or disprove the effectiveness of ANNs. This application will determine, ultimately, whether ANN is more accurate than multivariate linear regression in the prediction of prostate cancer progression. Appropriate statistical models are important to combine clinically the results of an array of biomarkers and cancer predictors. The specific aims are: (1) To develop an ANN-based method for the prediction of pathologic stage and to compare with the Partin nomogram - a clinically accepted multivariate linear regression-based method. (2) To develop a novel method that will add 95% confidence intervals to the ANN prediction of prostate cancer progression. (3) To develop an ANN-based model for the prediction of pathologic stage based on preoperative serial PSA measurements. The research design is to develop ANN-based predictive models and compare them to a clinically accepted standard and previously published results that were considered promising but had produced limited clinical use. The methods to be used include collection of a clinical database, analysis of artificial neural network and multivariate linear regression models, receiver operating characteristic (ROC) analysis, statistical estimation, and computation of confidence intervals.