Oncotarget

Research Papers:

Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas

Kevin Li-Chun Hsieh, Cheng-Yu Chen and Chung-Ming Lo _

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Oncotarget. 2017; 8:45888-45897. https://doi.org/10.18632/oncotarget.17585

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Abstract

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

Correspondence to:

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

ABSTRACT

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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