Author:
Hagiwara Akifumi,Tatekawa Hiroyuki,Yao Jingwen,Raymond Catalina,Everson Richard,Patel Kunal,Mareninov Sergey,Yong William H.,Salamon Noriko,Pope Whitney B.,Nghiemphu Phioanh L.,Liau Linda M.,Cloughesy Timothy F.,Ellingson Benjamin M.
Abstract
AbstractThis study aimed to differentiate isocitrate dehydrogenase (IDH) mutation status with the voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and to discover biological underpinnings of the clusters. A total of 69 patients with treatment-naïve diffuse glioma were scanned with pH-sensitive amine chemical exchange saturation transfer MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach was used for feature extraction from acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH status. The highest performance to predict IDH mutation status was found for 10-class clustering, with a mean area under the curve, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. Targeted biopsies revealed that the tissues with labels 7–10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. In conclusion, A machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.
Funder
UCLA SPORE in Brain Cancer
American Cancer Society
University of California Research Coordinating Committee
UCLA Jonsson Comprehensive Cancer Center Seed Grant
National Institutes of Health
Publisher
Springer Science and Business Media LLC
Cited by
6 articles.
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