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
• DOI: 10.1002/jmri.26010
• ISSN: 1053-1807; 1522-2586

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 grading potential of different MRI sequences or parametric maps. Study Type Retrospective; radiomics. Population A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively. Field Strength/Sequence 3.0T MRI/T1‐weighted images before and after contrast‐enhanced, T2‐weighted, multi‐b‐value diffusion‐weighted and 3D arterial spin labeling images. Assessment After multiparametric MRI preprocessing, high‐throughput features were derived from patients' volumes of interests (VOIs). The support vector machine‐based recursive feature elimination was adopted to find the optimal features for low‐grade glioma (LGG) vs. high‐grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency. Statistical Tests Student's t‐test or a chi‐square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist. Results Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI. Data Conclusion Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision‐making for patients with varied glioma grades. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518–1528