文章基本信息

标题：Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy
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作者：Takuya Mizutani ; Taiki Magome ; Hiroshi Igaki 等
期刊名称：Journal of Radiation Research
印刷版ISSN：0449-3060
电子版ISSN：1349-9157
出版年度：2019
卷号：60
期号：6
页码：818-824
DOI：10.1093/jrr/rrz066
摘要：ABSTRACT The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual patient by using a machine learning model. A total of 35 patients with malignant glioma were included in this study. The candidate features included 12 clinical features and 192 dose–volume histogram (DVH) features. The appropriate input features and parameters of the support vector machine (SVM) were selected using the genetic algorithm based on Akaike’s information criterion, i.e. clinical, DVH, and both clinical and DVH features. The prediction accuracy of the SVM models was evaluated through a leave-one-out cross-validation test with residual error, which was defined as the absolute difference between the actual and predicted survival times after radiotherapy. Moreover, the influences of various values of prescription dose and treatment duration on the predicted survival time were evaluated. The prediction accuracy was significantly improved with the combined use of clinical and DVH features compared with the separate use of both features (P < 0.01, Wilcoxon signed rank test). Mean ± standard deviation of the leave-one-out cross-validation using the combined clinical and DVH features, only clinical features and only DVH features were 104.7 ± 96.5, 144.2 ± 126.1 and 204.5 ± 186.0 days, respectively. The prediction accuracy could be improved with the combination of clinical and DVH features, and our results show the potential to optimize the treatment strategy for individual patients based on a machine learning model.
关键词：clinical features;dose–volume histogram features;malignant glioma;support vector machine;survival time prediction