Correlation between Multimodal Radiographic Features and Preoperative Seizure in Brain Tumor using Machine Learning

Abstract

Tumor-related epilepsy (TRE) refers to the condition in which primary brain tumors cause recurring seizures. A model that classifies brain tumors as epileptogenic or non-epileptogenic could improve prognosis and treatment methods for TRE. This study aims to identify which MRI sequences and machine learning algorithms (MLAs) could be used to build the most accurate epileptogenic tumor classification model. T1W, T2W, T2W FLAIR and T1W contrast-enhanced scans were acquired from 24 glioma patients, 8 with and 16 without pre-operative epilepsy. A total of 88 features were extracted from the MRI sequences, including tumor location, volume, and several first order textural features derived from gray level co-occurrence matrices (GLCM). Each feature was then used as a predicting variable for 9 MLAs (7 variants of support vector machines (SVMs) and 2 variants of logistic regression) to construct classification models. The top 11 classification models had testing accuracies above or equal to 75%. These models all used SVM variants instead of logistic regression variants. The classification model that used tumor location, and the one that used tumor volume, had testing accuracies of 100% and 87.5% respectively. The 9 other top classification models used GLCM features extracted from the contrast T1W sequence.Clinical Relevance—Our study showed that models which used SVMs were more accurate at classifying tumors by epileptogenicity than those that used logistic regression variants, and contrast T1W radiographic features could also be used in epileptogenic tumor classification models.