This study demonstrates the significance of integrating multi-parametric MRI attributes and effective machine learning techniques in preoperative glioma grading. A comprehensive scheme combining tumor attribute extraction, attribute selection and classification model was proposed and tested. The tumor attributes were collected from histogram and texture analysis of multi-parameter MRI maps within the whole tumor. The classification performances of 25 commonly used classifiers combined with 8 kinds of attribute selection strategies in differentiating low grade gliomas from high grade gliomas were investigated. Support vector machine (SVM) combined with SVM-RFE attribute selection method were found to exhibit superior performance to others.
This abstract and the presentation materials are available to members only; a login is required.