BREAST CANCER PATIENT SURVIVAL PREDICTION--A NEURAL NETWORK APPROACH

在1991财政年度，我们报告了开发回差的初步经验 传播人工神经网络（ANN）预测生存与 使用作为解释协变量的小型（170例）乳腺癌数据库 肿瘤大小、阳性淋巴结数量、组织学分级和雌激素 和孕激素受体状态。 我们得出结论，个别患者 可以计算生存曲线，并且可以 用于计算较大数据库的生存置信区间。 1992财政年度的进展包括将考克斯回归模型与 人工神经网络方法的生存分析，导致的结论是，人工神经网络 这种方法不会受到比例风险假设的限制， 考克斯模型，从而提出的因素，预测协会 目前还不清楚。 与现任主席Donald Henson博士（NCI）开始了合作， 美国癌症联合委员会（AJCC） 汉森博士和AJCC 对适当的计算方法的应用非常感兴趣 用于患者预后预测的预后因素 和管理 编写了计算群体精算生存函数的程序 基于截尾和 非删失事件。 研究是利用从NCI监测中提取的数据进行的， 流行病学，最终结果（SEER）计划。 6,000例黑色素瘤数据库 用于演示ANN生存预测的基本概念。 的 去年开发的反向误差传播人工神经网络被改进并应用于 44，000例乳腺癌数据库用于展示ANN生存预测 基于多种解释因素。 在AJCC年会上介绍了方法和结果 1992年1月在圣地亚哥，在AJCC多个工作组之前， 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.