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: 3853632
• 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/3853632
• 关键词: artificial intelligence; breast neoplasms; cancer information system; cancer risk; human mortality; neoplasm /cancer relapse /recurrence; prognosis

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.

乳腺癌生存预测目前是基于几个传统的 预后因素如肿瘤大小、淋巴结受累、雌激素和 孕酮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 的肿瘤相关抗原的预测结果正在研究中， 淋巴结阴性患者 神经网络在以下方面非常成功： 类似的多维模式分类问题，在许多 并且可用于乳腺癌复发， 生存预测和相关临床决策。 此外如 新的预后因素很容易被证实，它们可以很容易地被纳入 在神经网络方法中。 探索这些想法作为基础 一个新的项目，反向误差传播神经网络预测 患者无病生存率使用经典的、公认的 上面列出的参数。 训练和测试网络 对170例乳腺癌患者的临床资料进行了分析 在都灵，意大利地区。 我们感谢Cappa博士，Liscia和 南方的加格里亚。都灵的乔瓦尼医院收集和分享 他们的数据. 在传输神经网络时出现了几个问题 乳腺癌等临床医学分类技术问题 癌症复发和生存预测。 这些关切包括： 医学应用中经常遇到的数据稀疏问题， 在医疗决策中需要信任措施和解释-- 神经网络通常不提供的信息。 我们的经验导致 我们得出几个结论：（1）输入参数应按比例缩放，（2） 应在每个重要时间点计算权重，（3）改善 需要权重收敛方法，（4）单个患者生存率与 时间图是理想的输出，（5）需要置信区间， 可以用自举方法计算，（6）更大的数据库， 需要建立有意义的置信区间，（7）更高的性能 需要计算来处理较大的数据集和置信区间， 以及（8）训练数据中的删失后生存概率可以是 使用Kaplan-Meier生存分析方法完成。