Neural Network Prediction of Prostate Cancer Progression

前列腺癌进展的神经网络预测

• 批准号: 6603799
• 负责人: Yulei Jiang
• 金额: $ 14.75万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6603799
• 关键词: artificial intelligence; clinical research; computer assisted diagnosis; computer data analysis; human data; mathematical model; method development; model design /development; neoplasm /cancer classification /staging; neoplasm /cancer diagnosis; neoplastic process; outcomes research; prognosis; prostate neoplasms

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.

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）可以准确地预测前列腺癌的进展。这项研究的意义和健康相关性在于，准确预测前列腺癌进展对于识别手术非常有效的器官局限性前列腺癌患者以及手术效果较差但具有不必要并发症风险的更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。以前的人工神经网络的研究往往依赖于高度选择的人工神经网络，不能证明或反驳人工神经网络的有效性。该应用程序将最终确定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联合收割机临床上结合一系列生物标志物和癌症预测因子的结果是重要的。具体目标是： (1)建立一种基于人工神经网络的病理分期预测方法，并与临床上公认的基于多元线性回归的Partin诺模图进行比较。 (2)开发一种新的方法，将增加95%的置信区间的人工神经网络预测前列腺癌的进展。 (3)建立一个基于人工神经网络的模型，用于根据术前连续PSA测量值预测病理分期。 研究设计是开发基于人工神经网络的预测模型，并将其与临床公认的标准和先前发表的结果进行比较，这些结果被认为是有前途的，但临床应用有限。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、受试者工作特征（ROC）分析、统计估计和置信区间的计算。