Neural Network Prediction of Prostate Cancer Progression

描述（由申请人提供）：本申请的广泛、长期目标是通过准确诊断和准确评估肿瘤进展来有效治疗前列腺癌。在这个项目中要测试的假设是人工神经网络（ann）可以准确预测前列腺癌的进展。这项研究的意义和健康相关性在于，准确预测前列腺癌的进展对于确定手术非常有效的器官局限性前列腺癌患者和手术效果较差但有不必要并发症风险的晚期前列腺癌患者是非常重要的，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研究通常依赖于高度选择的人工神经网络，这些人工神经网络不能证明或否定人工神经网络的有效性。这一应用将最终确定人工神经网络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结合一系列生物标志物和癌症预测因子的临床结果非常重要。具体目标是：

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.