# Integrating Radiomics and Lesion Mapping for Cerebellar Mutism Syndrome Prediction

## TL;DR

This study combines MRI-based radiomics and clinical data to predict cerebellar mutism syndrome in children with posterior fossa tumors.

## Contribution

A novel hybrid model integrating radiomics, lesion mapping, and clinical factors for predicting cerebellar mutism syndrome in pediatric patients.

## Key findings

- A composite model using radiomic features and clinical variables achieved high predictive performance (AUCs between 0.81 and 0.87).
- Five radiomic features from tumor and intersection areas, along with gender, location, and weight, were significant predictors of CMS.
- Combining radiomics with lesion location weight improved the accuracy of CMS prediction.

## Abstract

Objective: To develop and validate a composite model that combines lesion–symptom mapping (LSM), radiomic information, and clinical factors for predicting cerebellar mutism syndrome in pediatric patients suffering from posterior fossa tumors. Methods: A retrospective analysis was conducted on a cohort of 247 (training set, n = 174; validation set, n = 73) pediatric patients diagnosed with posterior fossa tumors who underwent surgery at Beijing Children’s Hospital. Presurgical MRIs were used to extract the radiomics features and voxel distribution features. Clinical factors were derived from the medical records. Group comparison was used to identify the clinical risk factors of CMS. Combining location weight, radiomic features from tumor area and the significant intersection area, and clinical variables, hybrid models were developed and validated using multiple machine learning models. Results: The mean age of the cohort was 4.88 [2.89, 7.78] years, with 143 males and 104 females. Among them, 73 (29.6%) patients developed CMS. Gender, location, weight, and five radiomic features (three in the tumor mask area and two in the intersection area) were selected to build the model. The four models, KNN model, GBM model, RF model, and LR model, achieved high predictive performance, with AUCs of 0.84, 0.83, 0.81, and 0.87, respectively. Conclusions: CMS can be predicted using MRI features and clinical factors. The combination of radiomics and tumoral location weight could improve the prediction of CMS.