Breast Cancer Risk Assessment with Bayesian Networks

使用贝叶斯网络进行乳腺癌风险评估

• 批准号: 6667207
• 负责人: OMOLOLA I OGUNYEMI
• 金额: $ 6.85万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-30 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6667207
• 关键词: artificial intelligence; breast neoplasms; cancer risk; clinical research; computer data analysis; computer program /software; computer system design /evaluation; data management; early diagnosis; family genetics; female; human subject; mathematical model; model design /development; neoplasm /cancer epidemiology; statistics /biometry; women's health

DESCRIPTION (provided by applicant): Each year, a significant number of women in the United States are diagnosed with breast cancer. The ability to identify women at high risk for developing breast cancer as early as is possible would be invaluable for monitoring and management of this disease. As more information becomes available about the roles that different genetic, environmental, and personal health status factors play in determining breast cancer risk, it is important to develop methods for individualized breast cancer risk prediction that incorporate these factors. The goal of this application is to develop methods for individualized breast cancer risk prediction, using Bayesian networks with time dependencies, for women between the ages of 20 and 70 years. Bayesian networks provide methods for reasoning under conditions of uncertainty based on artificial intelligence and statistical principles. They allow for inclusion of expert opinion and empirical results from studies for a condition of interest. Specifically, we propose to (1) develop a Bayesian network based risk assessment tool for women aged 20 to 70 years, using knowledge provided by a panel of experts involved in cancer assessment and treatment research as well as relevant probabilities gleaned from the breast cancer literature; (2) perform a preliminary assessment of the accuracy of the risk assessment tool developed, using data obtained from 40 patients followed for at least 5 years through a high risk screening facility (the Yale Cancer Center Genetic Counseling Shared Resource); and (3) identify the steps required for a more formative validation of the risk assessment tool on a larger study population, and examine how the tool can be integrated with other quantitative methods designed for breast cancer risk prediction, such as Markov models.

描述（由申请人提供）： 每年，在美国有相当数量的女性被诊断患有乳腺癌。尽早识别患乳腺癌高危妇女的能力对于监测和管理这种疾病将是非常宝贵的。随着越来越多的信息变得不同的遗传，环境和个人健康状况因素在确定乳腺癌风险中发挥的作用，重要的是要开发出将这些因素纳入个体化乳腺癌风险预测的方法。该应用程序的目标是开发个性化的乳腺癌风险预测方法，使用具有时间依赖性的贝叶斯网络，针对20至70岁之间的女性。贝叶斯网络提供了基于人工智能和统计原理的不确定条件下的推理方法。它们允许纳入专家意见和研究的经验结果，以满足感兴趣的条件。具体而言，我们建议（1）利用参与癌症评估和治疗研究的专家小组提供的知识以及从乳腺癌文献中收集的相关概率，为20至70岁的女性开发基于贝叶斯网络的风险评估工具;（2）对所开发的风险评估工具的准确性进行初步评估，使用通过高风险筛查设施随访至少5年的40名患者的数据（耶鲁癌症中心遗传咨询共享资源）;以及（3）确定在更大的研究人群中对风险评估工具进行更多形成性验证所需的步骤，并检查该工具如何与设计用于乳腺癌风险预测的其他定量方法相结合，比如马尔可夫模型。