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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