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Proceedings Paper

IDH mutation assessment of glioma using texture features of multimodal MR images
Author(s): Xi Zhang; Qiang Tian; Yu-Xia Wu; Xiao-Pan Xu; Bao-Juan Li; Yi-Xiong Liu; Yang Liu; Hong-Bing Lu
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Paper Abstract

Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341S (3 March 2017); doi: 10.1117/12.2254212
Show Author Affiliations
Xi Zhang, Fourth Military Medical Univ. (China)
Qiang Tian, Tangdu Hospital (China)
Yu-Xia Wu, Fourth Military Medical Univ. (China)
Xiao-Pan Xu, Fourth Military Medical Univ. (China)
Bao-Juan Li, Fourth Military Medical Univ. (China)
Yi-Xiong Liu, Xijing Hospital (China)
Yang Liu, Fourth Military Medical Univ. (China)
Hong-Bing Lu, Fourth Military Medical Univ. (China)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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