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《Beijing Biomedical Engineering》 2019-03
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Grading method of gliomas based on Hurst index

YANG Yuxuan;TAO Ling;QIAN Zhiyu;College of Automation Engineering,Nanjing University of Aeronautics and Astronautics;  
Objective To describe the MRI brain rest data of glioma patients based on complexity analysis,and to find objective indicators of tumor grading based on complexity analysis. Methods Based on the complexity of the Hurst index analysis method,the functional information of fMRI images of brain tumors was extracted and analyzed,and the tumors were graded. Firstly,based on the MRIcro software,the corresponding regions of the tumor in the patient's tumor region,contralateral normal region,and normal control group were extracted; then the Hurst index was calculated for the extracted region; then the Hurst index value of the tumor region and its contralateral normal region was performed. Statistical analysis was performed on the Hurst index values in the same region of the tumor region and the control group. Finally,29 tumor patients were grouped according to pathological grade,including 10 primary tumor patients,7 secondary tumor patients,6 third-and fourth-grade tumor patients,and two-sample statistical analysis was performed on the Hurst index of different groups. Results The Hurst index value of the tumor region was proportional to the tumor grade. The higher the tumor grade was,the higher the Hurst index value was. The statistical analysis showed that the Hurst index of the tumor areas at different levels was significantly different.The Hurst index ranged from 0. 638 1 to 0. 673 7 in low-grade tumors,and from 0. 751 4 to 0. 819 4 in highgrade tumors. Conclusions The Hurst index analysis method can distinguish between low-grade and high-grade gliomas,and can provide help for more detailed classification of gliomas.
【Fund】: 南京航空航天大学研究生创新基地(实验室)开放基金(kfjj20170324)资助
【CateGory Index】: R739.41
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【Citations】
Chinese Journal Full-text Database 1 Hits
1 BAI Ling;JING Bin;YE Derong;School of Biomedical Engineering,Capital Medical University;;Age effects on the complexity of brain endogenous oscillations in f MRI[J];北京生物医学工程;2015-01
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