Eur J Radiol 2019 Mar 24;112:169-179. Epub 2019 Jan 24.
Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
Objective: To evaluate the differentiation efficiency of texture analysis of T1WI, T2WI and contrasted-enhanced T1WI MRI sequences in gliomas with and without IDH1 mutation based on entire tumor region.
Materials And Methods: A total of 42 patients with histopathologically confirmed gliomas, including 21 patients carrying IDH1 mutation (IDH1 group) and 21 with wild-type IDH1 (IDH1 group) were included in this study. The preoperative MRI and clinical data were collected. The regions of interest (ROIs) covering the entire tumor and edema were manually delineated on axial slices using O.K. (Omni Kinetics, GE Healthcare, China) software; and the histogram and GLCM features based on T1WI, T2WI and contrasted-enhanced T1WI sequences were automatically generated.
Results: Based on contrasted-enhanced T1WI features, the inertia resulted as the best feature for diagnosis, with the AUC of 0.844. Furthermore, the AUC for gliomas prediction with IDH1 was 0.800 for cluster prominence. IDH1-mutation was differentiated on T2WI with the highest AUC of 0.848, which corresponded to GLCM Entropy. After modeling, the accuracy of the contrasted-enhanced T1WI, T1WI, and T2WI features model was 0.952, 0.857, and 0.738, respectively. The AUC of Joint Variable for predicting IDH1 was 0.984, while the AUC of Joint Variable for predicting the same mutation was 0.927. The diagnostic efficiency of Joint Variable was also desirable.
Conclusion: MRI texture analysis could be used as a new noninvasive method for identification of gliomas with IDH1 mutation. The present results show that the Joint Variable derived from conventional MR imaging histogram and GLCM features is suitable for precise detection of IDH1-mutated gliomas.
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