Feng-Chi Chang: Department of Radiology, School of Medicine, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University, Taipei, Taiwan.
Tai-Tong Wong: Department of Neurosurgery, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.
Kuo-Sheng Wu: Department of Neurosurgery, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.
Chia-Feng Lu: Department of Biomedical Imaging and Radiological Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Ting-Wei Weng: Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan.
Muh-Lii Liang: Department of Neurosurgery, Mackay Memorial Hospital, Taipei, Taiwan.
Chih-Chun Wu: Department of Radiology, School of Medicine, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University, Taipei, Taiwan.
Wan Yuo Guo: Department of Radiology, School of Medicine, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University, Taipei, Taiwan.
Cheng-Yu Chen: Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan.
Kevin Li-Chun Hsieh: Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan. ORCID
PURPOSE: Medulloblastoma (MB) is a highly malignant pediatric brain tumor. In the latest classification, medulloblastoma is divided into four distinct groups: wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We analyzed the magnetic resonance imaging radiomics features to find the imaging surrogates of the 4 molecular subgroups of MB. MATERIAL AND METHODS: Frozen tissue, imaging data, and clinical data of 38 patients with medulloblastoma were included from Taipei Medical University Hospital and Taipei Veterans General Hospital. Molecular clustering was performed based on the gene expression level of 22 subgroup-specific signature genes. A total 253 magnetic resonance imaging radiomic features were generated from each subject for comparison between different molecular subgroups. RESULTS: Our cohort consisted of 7 (18.4%) patients with WNT medulloblastoma, 12 (31.6%) with SHH tumor, 8 (21.1%) with Group 3 tumor, and 11 (28.9%) with Group 4 tumor. 8 radiomics gray-level co-occurrence matrix texture (GLCM) features were significantly different between 4 molecular subgroups of MB. In addition, for tumors with higher values in a gray-level run length matrix feature-Short Run Low Gray-Level Emphasis, patients have shorter survival times than patients with low values of this feature (p = 0.04). The receiver operating characteristic analysis revealed optimal performance of the preliminary prediction model based on GLCM features for predicting WNT, Group 3, and Group 4 MB (area under the curve = 0.82, 0.72, and 0.78, respectively). CONCLUSION: The preliminary result revealed that 8 contrast-enhanced T1-weighted imaging texture features were significantly different between 4 molecular subgroups of MB. Together with the prediction models, the radiomics features may provide suggestions for stratifying patients with MB into different risk groups.
References
Neuroimage. 2017 Jan 15;145(Pt B):166-179
[PMID: 27989847]
Cancer Cell. 2017 Jun 12;31(6):737-754.e6
[PMID: 28609654]
