BREAST CANCER PATIENT SURVIVAL PREDICTION--A NEURAL NETWORK APPROACH

乳腺癌患者的生存预测——神经网络方法

• 批准号: 3838534
• 负责人: J M DELEO
• 金额: --
• 依托单位: CENTER FOR INFORMATION TECHNOLOGY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/3838534
• 关键词: artificial intelligence; breast neoplasms; cancer information system; cancer risk; computer system design /evaluation; human mortality; mathematical model; neoplasm /cancer epidemiology; prognosis

In FY91 we reported preliminary experiences in developing a back-error propagation Artificial Neural Network (ANN) to predict survival with a small (170 cases) breast cancer database using as explanatory covariates tumor size, number of positive lymph nodes, histologic grade, and estrogen and progesterone receptor status. We concluded that individual patient survival curves could be computed and that bootstrapping methods could be used to compute survival confidence intervals with larger databases. Progress in FY92 included a comparison of the Cox regression model with an ANN approach to survival analysis, leading to the conclusion that an ANN approach would not be constrained by the proportional hazards assumption of the Cox model, thus suggesting factors for which predictive associations are not yet known. A collaboration was begun with Dr. Donald Henson (NCI), present chairman of the American Joint Committee on Cancer (AJCC). Dr. Henson and the AJCC are very interested in the application of appropriate computing methodologies to prognostic factors for evaluation and use in patient outcome prediction and management. A program was written for computing group actuarial survival functions based on the assumption of equal interval hazard rates for censored and non-censored events. Studies were conducted with data extracted from the NCI Surveillance, Epidemiology, End Results (SEER) program. A 6,000-case melanoma database was used to demonstrate basic concepts in ANN survival prediction. The back-error propagation ANN developed last year was refined and applied to a 44,000-case breast cancer database to demonstrate ANN survival prediction based on multi-explanatory factors. Presentations of methods and results were made at the AJCC annual Meeting in January 1992 in San Diego and before the AJCC Task Force on Multiple Prognostic Factors in Chicago in June 1992.

在91财年，我们报告了发展后纠正的初步经验 传播人工神经网络（ANN），以预测 小（170例）乳腺癌数据库使用AS解释性协变量 肿瘤大小，阳性淋巴结的数量，组织学等级和雌激素 和孕酮受体状态。 我们得出的结论是个人患者 可以计算生存曲线，并可以进行自举方法 用于用较大数据库计算生存置信区间。 FY92中的进展包括对Cox回归模型的比较 ANN生存分析的方法，得出结论，即ANN 方法不会受到比例危害假设的限制 COX模型，因此提出了预测性关联的因素 尚不清楚。 与唐纳德·亨森（NCI）博士始于现任主席Donald Henson博士（NCI） 美国癌症联合委员会（AJCC）。 Henson博士和AJCC是 对应用适当的计算方法非常感兴趣 在患者结果预测中评估和使用的预后因素 和管理。 编写了用于计算集团精算生存功能的程序 基于对审查的假设相等的间隔危险率 未进行的事件。 研究是用从NCI监视中提取的数据进行的 流行病学，最终结果（SEER）计划。 一个6,000盘黑色素瘤数据库 被用来证明ANN生存预测中的基本概念。 这 Ann于去年开发的后校园传播进行了完善并应用于 一个44,000例乳腺癌数据库，以证明ANN存活预测 基于多解释性因素。 方法和结果的演示是在AJCC年会上进行的 1992年1月在圣地亚哥和AJCC工作队之前的多个 1992年6月，芝加哥的预后因素。