Meeting Banner
Abstract #4556

Investigation of machine learning techniques in preoperative glioma grading based on multi-parametric MRI data

Xin Zhang1, Linfeng Yan1, Yang Yang1, Haiyan Nan1, Yu Han1, Yuchuan Hu1, Jin Zhang1, Ying Yu1, Yingzhi Sun1, Qian Sun1, Zhicheng Liu1, Wen Wang1, and Guangbin Cui1

1Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, People's Republic of China

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.

Join Here