Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas
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Kevin Li-Chun Hsieh1,2,*, Cheng-Yu Chen1,2,3,* and Chung-Ming Lo4,5
1Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
2Research Center of Translational Imaging, College of Medicine, Taipei Medical University, Taipei, Taiwan
3Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
4Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
5Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
*These authors are co-first authors based on equal contributions in this study
Chung-Ming Lo, email: [email protected]
Keywords: isocitrate dehydrogenase, brain tumor, glioblastoma, computer-aided diagnosis, magnetic resonance imaging
Received: January 24, 2017 Accepted: April 14, 2017 Published: May 03, 2017
The present study proposed a computer-aided diagnosis system based on radiomic features extracted through magnetic resonance imaging to determine the isocitrate dehydrogenase status in glioblastomas. Magnetic resonance imaging data were obtained from 32 patients with wild-typeisocitrate dehydrogenase and 7 patients with mutant isocitrate dehydrogenase in glioblastomas. Radiomic features, namely morphological, intensity, and textural features, were extracted from the tumor area of each patient. The feature sets were evaluated using a logistic regression classifier to develop a prediction model. The accuracy of the global morphological and intensity features was 51% (20/39) and 59% (23/39), respectively. The textural features describing local patterns yielded an accuracy of 85% (33/39), which is significantly higher than that yielded by the morphological and intensity features. The agreement level (κ) between the prediction results and biopsy-proven pathology was 0.60. The proposed diagnosis system based on radiomic textural features shows promise for application in providing suggestions to radiologists for distinguishing isocitrate dehydrogenase mutations in glioblastomas.
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