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Abstract #3084

Predicting molecular subgroups of medulloblastoma using a quantitative radiomics approach

Jing Yan1, Haiyang Geng2, Binke Yuan3, Zhenyu Zhang4, and Jingliang Cheng1

1MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 2BCN Neuroimaging Center, University Medical Center Groningen, University of Groningen, Groningen, Netherlands, 3Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China, 4Neurosurgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

Machine learning-based radiomics have been introduced in providing information on molecular biology and genomics of tumors. Here, we used features of MRI to predict molecular subgroups of medulloblastoma. MRI-based radiomics features were extracted from 37 patients with medulloblastoma (WNT = 11, SHH = 9, Group 3 = 8 , and Group 4 = 9). The molecular subgroups of medulloblastoma were classified with accepted accuracies by using support vector machine (SVM). In conclusion, MRI-based radiomics can effectively predict molecular subgroups of medulloblastoma using the machine-learning approach to benefit the treatment and prognosis of medulloblastoma.

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