• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Medical Physics >Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis
【2h】

Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis

机译：支持向量机算法预测肺放射性肺炎的研究

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The purpose of this study is to build and test a support vector machine (SVM) model to predict for the occurrence of lung radiation-induced Grade 2+ pneumonitis. SVM is a sophisticated statistical technique capable of separating the two categories of patients (with/without pneumonitis) using a boundary defined by a complex hypersurface. Despite the complexity, the SVM boundary is only minimally influenced by outliers that are difficult to separate. By contrast, the simple hyperplane boundary computed by the more commonly used and related linear discriminant analysis method is heavily influenced by outliers. Two SVM models were built using data from 219 patients with lung cancer treated using radiotherapy (34 diagnosed with pneumonitis). One model (SVMall) selected input features from all dose and non-dose factors. For comparison, the other model (SVMdose) selected input features only from lung dose-volume factors. Model predictive ability was evaluated using ten-fold cross-validation and receiver operating characteristics (ROC) analysis. For the model SVMall, the area under the cross-validated ROC curve was 0.76 (sensitivity/specificity =74%/75%). Compared to the corresponding SVMdose area of 0.71 (sensitivity/specificity =68%/68%), the predictive ability of SVMall was improved, indicating that non-dose features are important contributors to separating patients with and without pneumonitis. Among the input features selected by model SVMall, the two with highest importance for predicting lung pneumonitis were: (a) generalized equivalent uniform doses close to the mean lung dose, and (b) chemotherapy prior to radiotherapy. The model SVMall is publicly available via internet access.
机译：这项研究的目的是建立和测试支持向量机（SVM）模型，以预测发生肺部辐射诱发的2+级肺炎的可能性。 SVM是一种复杂的统计技术，能够使用复杂的超曲面定义的边界将两类患者（有/无肺炎）分开。尽管复杂，但SVM边界仅受难以分离的异常值的影响最小。相比之下，由更常用和相关的线性判别分析方法计算出的简单超平面边界受到异常值的严重影响。利用来自219例接受放射治疗的肺癌患者的数据（34例诊断为肺炎）建立了两个SVM模型。一种模型（SVMall）从所有剂量和非剂量因素中选择输入特征。为了进行比较，其他模型（SVMdose）仅从肺部剂量-体积因子中选择了输入特征。使用十倍交叉验证和接收者操作特征（ROC）分析评估模型的预测能力。对于模型SVMall，交叉验证的ROC曲线下的面积为0.76（敏感性/特异性= 74％/ 75％）。与相应的SVMdose面积0.71（敏感度/特异性= 68％/ 68％）相比，SVMall的预测能力得到了改善，表明非剂量特征是区分有无肺炎患者的重要因素。在模型SVMall选择的输入特征中，对预测肺部肺炎最重要的两个特征是：（a）接近平均肺部剂量的通用等效剂量，以及（b）放疗前的化疗。 SVMall型号可通过互联网公开获得。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号