Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

Abstract

// Xin Zhang 1, * , Lin-Feng Yan 1, * , Yu-Chuan Hu 1 , Gang Li 2 , Yang Yang 1 , Yu Han 1 , Ying-Zhi Sun 1 , Zhi-Cheng Liu 1 , Qiang Tian 1 , Zi-Yang Han 3 , Le-De Liu 3 , Bin-Quan Hu 3 , Zi-Yu Qiu 3 , Wen Wang 1 and Guang-Bin Cui 1 1 Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an 710038, Shaanxi, P.R. China 2 Department of Neurosurgery, Tangdu Hospital, The Fourth Military Medical University, Xi’an 710038, Shaanxi, P.R. China 3 Student Brigade, The Fourth Military Medical University, Xi’an 710032, Shaanxi, P.R. China * These authors have contributed equally to this work Correspondence to: Guang-Bin Cui, email: [e-mail] Wen Wang, email: [e-mail] Keywords: glioma grading, MRI, machine learning, attribute selection, support vector machine (SVM) Received: March 17, 2017     Accepted: April 19, 2017     Published: May 18, 2017 ABSTRACT Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type , c , gama , K , etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.