Multi-sequence clinical-radiomic model predicts molecular subgroups of pediatric medulloblastoma / 中华实用儿科临床杂志

ABSTRACT

Objective:To explore the value of the model based on multi-sequence magnetic resonance imaging (MRI) radiomics and clinical features in predicting molecular subtypes of pediatric medulloblastoma (MB).Methods:MRI imaging data and clinical data of 100 children with primary MB admitted in the First Affiliated Hospital of Zhengzhou University from January 2011 to January 2020 were analyzed retrospectively.Fifty children with primary MB were allocated to training cohort, and those of the other 50 were allocated to testing cohort by using simple random sampling method.In the training cohort, there were 5 cases of WNT-activated MB (Wingless, WNT), 5 cases of SHH-activated MB (Sonic hedgehog, SHH), 28 cases of non-WNT/non-SHH medulloblastoma Group3 (Group3), 12 cases of non-WNT/non-SHH medulloblastoma Group4 (Group4). The testing cohort included 11 cases of WNT, 3 cases of SHH, 24 cases of Group3 and 12 cases of Group4.The robust and non-redundant features were selected from 5 929 three-dimensional radiomic features extracted from the manually delineated tumor area, and Boruta algorithm was used to further select the optimal features.Based on the selected features, a random forest prediction model was constructed using the training cohort (50 cases), which was further used to evaluate the testing cohort (50 cases). Combined with radiomic features and clinical features, a joint random forest prediction, clinical-radiomic model was constructed.Results:A radiomic model containing 13 optimal radiomics features was used to predict molecular subtypes of MB.The area under curve(AUC) of receiver operating characteristic (ROC) curve for WNT, SHH, Group3 and Group4 MB cases in the testing cohort was 0.923 1, 0.673 7, 0.519 2 and 0.705 0, respectively.Incorporating clinical features into the radiomic model improved AUC for WNT and SHH at 0.944 1 and 0.819 1, respectively.Conclusions:The multi-sequence clinical radiomic model has a high predictive value for pediatric MB with the molecular subtypes of WNT and SHH, which provides decision-making supports for individualized diagnosis and treatment of pediatric MB.