Radiomics strategy for glioma grading using texture features from multiparametric MRI

• Published in: Journal of magnetic resonance imaging Vol. 48; no. 6; pp. 1518 - 1528
• Main Authors: Tian, Qiang, Yan, Lin‐Feng, Zhang, Xi, Zhang, Xin, Hu, Yu‐Chuan, Han, Yu, Liu, Zhi‐Cheng, Nan, Hai‐Yan, Sun, Qian, Sun, Ying‐Zhi, Yang, Yang, Yu, Ying, Zhang, Jin, Hu, Bo, Xiao, Gang, Chen, Ping, Tian, Shuai, Xu, Jie, Wang, Wen, Cui, Guang‐Bin
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.12.2018
• Subjects: Classification; Data processing; Decision making; Evaluation; Feature extraction; Field strength; Glioma; glioma grading; Histograms; Image contrast; Image enhancement; Magnetic resonance imaging; Medical imaging; multiparametric MRI; Parameters; Patients; Population (statistical); Population studies; Radiomics; Spin labeling; Statistical analysis; Statistical tests; Support vector machines; SVM; Texture; texture feature; multiparametric MRI; glioma grading; radiomics; texture feature; SVM

Background Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays. Purpose/Hypothesis To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the gradi...