Machine Learning–Based Radiomics for Molecular Subtyping of Gliomas

Clinical Cancer Research - Tập 24 Số 18 - Trang 4429-4436 - 2018
Chia‐Feng Lu1,2,3, Fei‐Ting Hsu2,4,5, Kevin Li‐Chun Hsieh2,4,5, Yu‐Chieh Jill Kao2,5, Cheng‐Yu Chen4, Justin Bo‐Kai Hsu6, Ping‐Huei Tsai2,7,8, Ray‐Jade Chen9,10, Chao‐Ching Huang11,12,13, Yun Yen14
11Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.
22Research Center of Translational Imaging, College of Medicine, Taipei Medical University, Taipei, Taiwan.
33Department of Anatomy and Cell Biology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
44Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan.
55Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
66Department of Medical Research, Taipei Medical University Hospital, Taipei, Taiwan.
77Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan.
88Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan.
910Division of General Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan.
109Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
1111Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
1212Department of Pediatrics, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
1313Department of Pediatrics, Wan-Fang Hospital, Taipei Medical University, Taipei, Taiwan.
1414Ph.D. Program for Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.

Tóm tắt

Abstract Purpose: The new classification announced by the World Health Organization in 2016 recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase (IDH) and 1p/19q genotypes in addition to histologic phenotypes. We aim to determine whether clinical MRI can stratify these molecular subtypes to benefit the diagnosis and monitoring of gliomas. Experimental Design: The data from 456 subjects with gliomas were obtained from The Cancer Imaging Archive. Overall, 214 subjects, including 106 cases of glioblastomas and 108 cases of lower grade gliomas with preoperative MRI, survival data, histology, IDH, and 1p/19q status were included. We proposed a three-level machine-learning model based on multimodal MR radiomics to classify glioma subtypes. An independent dataset with 70 glioma subjects was further collected to verify the model performance. Results: The IDH and 1p/19q status of gliomas can be classified by radiomics and machine-learning approaches, with areas under ROC curves between 0.922 and 0.975 and accuracies between 87.7% and 96.1% estimated on the training dataset. The test on the validation dataset showed a comparable model performance with that on the training dataset, suggesting the efficacy of the trained classifiers. The classification of 5 molecular subtypes solely based on the MR phenotypes achieved an 81.8% accuracy, and a higher accuracy of 89.2% could be achieved if the histology diagnosis is available. Conclusions: The MR radiomics-based method provides a reliable alternative to determine the histology and molecular subtypes of gliomas. Clin Cancer Res; 24(18); 4429–36. ©2018 AACR.

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Clinical Cancer Research

Tập 24 Số 18

4429-4436

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