NDLI: Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET

Content Provider	Springer Nature : BioMed Central
Author	Tatekawa, Hiroyuki Hagiwara, Akifumi Uetani, Hiroyuki Bahri, Shadfar Raymond, Catalina Lai, Albert Cloughesy, Timothy F. Nghiemphu, Phioanh L. Liau, Linda M. Pope, Whitney B. Salamon, Noriko Ellingson, Benjamin M.
Abstract	Background The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsupervised, two-level clustering approach followed by support vector machine in order to classify the isocitrate dehydrogenase (IDH) status of gliomas. Methods Sixty-two treatment-naïve glioma patients who underwent FDOPA PET and MRI were retrospectively included. Contrast enhanced T1-weighted images, T2-weighted images, fluid-attenuated inversion recovery images, apparent diffusion coefficient maps, and relative cerebral blood volume maps, and FDOPA PET images were used for voxel-wise feature extraction. An unsupervised two-level clustering approach, including a self-organizing map followed by the K-means algorithm was used, and each class label was applied to the original images. The logarithmic ratio of labels in each class within tumor regions was applied to a support vector machine to differentiate IDH mutation status. The area under the curve (AUC) of receiver operating characteristic curves, accuracy, and F1-socore were calculated and used as metrics for performance. Results The associations of multiparametric imaging values in each cluster were successfully visualized. Multiparametric images with 16-class clustering revealed the highest classification performance to differentiate IDH status with the AUC, accuracy, and F1-score of 0.81, 0.76, and 0.76, respectively. Conclusions Machine learning using an unsupervised two-level clustering approach followed by a support vector machine classified the IDH mutation status of gliomas, and visualized voxel-wise features from multiparametric MRI and FDOPA PET images. Unsupervised clustered features may improve the understanding of prioritizing multiparametric imaging for classifying IDH status.
Related Links	https://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-021-00396-5.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14707330
DOI	10.1186/s40644-021-00396-5
Journal	Cancer Imaging
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2021-03-10
Access Restriction	Open
Subject Keyword	Oncology Cancer Research Imaging Radiology Nuclear Medicine 18F-DOPA PET Machine learning MRI IDH mutation Clustering Diffuse glioma
Content Type	Text
Resource Type	Article
Subject	Radiological and Ultrasound Technology Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.3/2023

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