Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy.

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

SUBMITTER: Mizutani T 

PROVIDER: S-EPMC7357235 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature