BREAST CANCER PATIENT SURVIVAL PREDICTION--A NEURAL NETWORK APPROACH

目前，乳腺癌的生存预测是基于几个传统的 预后因素，如肿瘤大小，淋巴转移，雌激素和 孕激素受体状态和组织学分级。新的预后因素 如增殖指数、组织蛋白酶D、基因突变和表达 正在研究肿瘤相关抗原对预测预后的作用 结节阴性患者。神经网络在以下领域取得了巨大的成功 多维模式分类中的相似问题 工程应用，可能对乳腺癌复发和 生存预测和相关的临床决策。此外，由于 新的预后因素很容易被证明，它们可以很容易地被纳入 在神经网络方法论中。探索这些思想作为基础 一个新的项目，反向误差传播神经网络来预测 使用经典的、完善的患者无病生存 开发了上述参数。培训和测试网络 对170例乳腺癌患者的临床资料进行了分析。 在意大利都灵地区。我们感谢卡帕博士、利西亚博士和 都灵S乔瓦尼医院的加利亚收藏和分享 他们的数据。在传输神经网络时出现了几个问题 技术对乳腺等临床医学分类问题的研究 癌症复发和生存预测。这些担忧包括： 医疗应用程序中经常遇到的数据稀疏性 在医疗决策中需要信任措施和解释-- 神经网络通常不会提供信息。我们的经验使我们 我们得出了几个结论：(1)输入参数应按比例调整，(2) 应在每个重要时间点计算权重，(3)改进 需要采用权重收敛方法，(4)单患者存活率与 时间曲线图是理想的输出，(5)需要置信度区间和 可以用自举方法计算，(6)更大的数据库是 需要建立有意义的置信度区间，(7)更高的性能 需要计算来处理更大的数据集和置信度区间， 和(8)训练数据中的后审查存活概率可以是 采用Kaplan-Meier生存分析方法完成。

Breast cancer survival prediction is presently based on several traditional prognostic factors such as tumor size, lymph node involvement, estrogen and progesterone receptor status and histologic grade. New prognostic factors such as proliferative index, cathepsin D, genetic mutations, and expression of tumor associated antigens are under study in predicting outcome for node-negative patients. Neural networks have been highly successful in analogous multi-dimensional pattern classification problems in many engineering applications and may be useful in breast cancer relapse and survival prediction and related clinical decision making. Furthermore, as new prognostic factors are easily proven, they could be easily incorporated within the neural network methodology. Exploring these ideas as the basis of a new project, a back error propagation neural network to predict patient disease-free survival using the classical, well-established parameters listed above was developed. Training and testing the network was performed with clinical data derived from 170 cases of breast cancers in the Torino, Italy area. We are grateful to Drs. Cappa, Liscia and Gaglia of the s. Giovanni Hospital in Torino for collecting and sharing their data. Several concerns arose when transporting neural network technology to clinical medicine classification problems such as breast cancer relapse and survival prediction. These concerns include: sparseness of data usually encountered in medical applications and the need for confidence measures and explanations in medical decisions-- information neural networks do not usually provide. Our experience has led us to several conclusions: (1) input parameters should be scaled, (2) weights should be computed at each significant time point, (3) improved weight convergence methods are needed, (4) single patient survival versus time plots are desirable outputs, (5) confidence intervals are needed and can be computed with bootstrapping methods, (6) larger data bases are needed to establish meaningful confidence intervals, (7) higher performance computing is needed to process larger data sets and confidence intervals, and (8) post-censor survival probabilities in training data can be completed using Kaplan-Meier survival analysis methods.