Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy
| Autor: | Kanabu Nawa, Hiroshi Igaki, Taiki Magome, Noriyasu Sekiya, Takuya Mizutani, Akihiro Haga, Keiichi Nakagawa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Time Factors clinical features Adolescent Wilcoxon signed-rank test Health Toxicology and Mutagenesis medicine.medical_treatment Absolute difference Residual Machine learning computer.software_genre Standard deviation Machine Learning Young Adult 03 medical and health sciences 0302 clinical medicine Histogram Regular Paper medicine Humans dose–volume histogram features Radiology Nuclear Medicine and imaging support vector machine Child Aged 030304 developmental biology Aged 80 and over 0303 health sciences Radiation business.industry Dose-Response Relationship Radiation Glioma malignant glioma Middle Aged Models Theoretical Survival Analysis Radiation therapy Support vector machine 030220 oncology & carcinogenesis Female Artificial intelligence Akaike information criterion business computer survival time prediction |
| Zdroj: | Journal of Radiation Research |
| ISSN: | 1349-9157 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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