A Comparison of Machine Learning Models for Survival Prediction of Patients with Glioma Using Radiomic Features from MRI Scans

Abstract

Abstract Background Glioma is a primary, malignant, highly aggressive brain tumor, with patients having an average life expectancy of 14 to 16 months after diagnosis. Magnetic resonance imaging (MRI) scans of these patients can be used to extract and analyze quantifiable features with potential clinical significance. We hypothesize that there is a correlation between radiomic features extracted from MRI scans and survival. Along with clinical data, the radiomic features could be used in survival prediction of patients, providing beneficial information for clinicians to design personalized treatment plans. Methods In our study, we have utilized 3D Slicer for tumor segmentation and feature extraction and performed survival prediction of patients with glioma using four different machine learning models. Results and Conclusion Among the models compared, we have achieved a maximum prediction accuracy of 64.4% using the k-nearest neighbors model, which was trained and tested on a combination of clinical data and radiomic features extracted from MRI images provided in the BraTS 2020 dataset.