Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features
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Xin Zhang1,*, Lin-Feng Yan1,*, Yu-Chuan Hu1, Gang Li2, Yang Yang1, Yu Han1, Ying-Zhi Sun1, Zhi-Cheng Liu1, Qiang Tian1, Zi-Yang Han3, Le-De Liu3, Bin-Quan Hu3, Zi-Yu Qiu3, Wen Wang1 and Guang-Bin Cui1
1Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an 710038, Shaanxi, P.R. China
2Department of Neurosurgery, Tangdu Hospital, The Fourth Military Medical University, Xi’an 710038, Shaanxi, P.R. China
3Student Brigade, The Fourth Military Medical University, Xi’an 710032, Shaanxi, P.R. China
*These authors have contributed equally to this work
Guang-Bin Cui, email: email@example.com
Wen Wang, email: firstname.lastname@example.org
Keywords: glioma grading, MRI, machine learning, attribute selection, support vector machine (SVM)
Received: March 17, 2017 Accepted: April 19, 2017 Published: May 18, 2017
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.
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