Presurgical MRI-Based Radiomics Models for Predicting Cerebellar Mutism Syndrome in Children With Posterior Fossa Tumors

Background: Current studies have indicated that tumoral morphologic features are associated with cerebellar mutism syndrome (CMS), but the radiomics application in CMS is scarce. Purpose: To develop a model for CMS discrimination based on multiparametric MRI radiomics in patients with posterior fossa tumors. Study Type: Retrospective. Population: A total of 218 patients (males 132, females 86) with posterior fossa tumors, 169 of which were included in the MRI radiomics analysis. The MRI radiomics study cohort (169) was split into training (119) and testing (50) sets with a ratio of 7:3. Field/Sequence: All the MRI were acquired under 1.5/3.0 T scanners. T2-weighted image (T2W), T1-weighted (T1W), fluid attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI). Assessment: Apparent diffusion coefficient (ADC) maps were generated from DWI. Each MRI dataset generated 1561 radiomics characteristics. Feature selection was performed with univariable logistic analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO) penalized logistic regression. Significant clinical features were selected with multivariable logistic analysis and used to constructed the clinical model. Radiomics models (based on T1W, T2W, FLAIR, DWI, ADC) were constructed with selected radiomics features. The mix model was based on the multiparametric MRI radiomics features. Statistical Test: Multivariable logistic analysis was utilized during clinical features selection. Models' performance was evaluated using the area under the receiver operating characteristic (AUC) curve. Interobserver variability was assessed using Cohen's kappa. Significant threshold was set as P<0.05. Results: Sex (aOR=3.72), tumor location (aOR=2.81), hydrocephalus (aOR=2.14), and tumor texture (aOR=5.08) were significant features in the multivariable analysis and were used to construct the clinical model (AUC=0.79); totally, 33 radiomics features were selected to construct radiomics models (AUC=0.63-0.93). Seven of the 33 radiomics features were selected for the mix model (AUC=0.93). Data Conclusion: Multiparametric MRI radiomics may be better at predicting CMS than single-parameter MRI models and clinical model.