Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma - Yan, Jing; Liu, Lei; Wang, Weiwei; Zhao, Yuanshen; Li, Kay Ka-Wai; Li, Ke; Wang, Li; Yuan, Binke; Geng, Haiyang; Zhang, Shenghai; Liu, Zhen; Duan, Wenchao; et al. (Frontiers in Oncology, 2020)
The 2016 WHO Classification of Central Nervous System tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3 and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations and clinical factors. 122 MB patients were enrolled retrospectively. After selecting robust, nonredundant and relevant features from 5529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining the radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004 and 0.6979 for SHH, Group 3 and Group 4 in testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70% and 86.67% for Group 3 and Group 4, respectively. The prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict molecular subgroups of MB.