BREAST CANCER PATIENT SURVIVAL PREDICTION--A NEURAL NETWORK APPROACH

在91财年，我们报告了开发反向错误的初步经验 用传播人工神经网络(ANN)预测存活率 使用小型(170例)乳腺癌数据库作为解释协变量 肿瘤大小、阳性淋巴结数、组织学分级和雌激素 和黄体酮受体状态。我们的结论是，个别患者 可以计算生存曲线，自举方法可以是 用于计算具有较大数据库的生存置信度区间。 1992财年的进展包括COX回归模型与 用人工神经网络进行生存分析，得出结论：一个人工神经网络 方法不会受到比例风险假设的限制 考克斯模型，从而提出了预测关联的因素 还不为人所知。 与现任主席唐纳德·汉森博士(NCI)开始合作 美国癌症联合委员会(AJCC)。Henson博士和AJCC 对适当计算方法的应用非常感兴趣 对预后因素进行评估，并在患者预后预测中使用 和管理。 编制了一个计算群体精算生存函数的程序 基于相同区间风险率的假设 不受审查的事件。 研究是使用从NCI监视中心提取的数据进行的， 流行病学，最终结果(SEER)计划。包含6000例黑色素瘤病例的数据库 用来说明人工神经网络生存预测的基本概念。这个 对去年提出的反向误差传播神经网络进行了改进，并将其应用于 一个包含44,000例乳腺癌病例的数据库来证明ANN的生存预测 基于多个解释因素。 在AJCC年会上介绍了方法和结果 1992年1月在圣地亚哥，在AJCC关于多个 1992年6月在芝加哥的预后因素。

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.