Development and Internal Validation of a Machine Learning-Enhanced Nomogram to Stratify Risk of Chronic Cognitive Impairment Following Pediatric Posterior Fossa Surgery

The goal of this observational study is to develop and validate a clinical prediction model to identify risk factors for long-term cognitive dysfunction in children (ages 0-18 years) who have undergone surgical resection of a posterior fossa tumor. The main questions it aims to answer are:

Can a combination of preoperative and postoperative clinical, surgical, and neuroimaging factors accurately predict which children will develop long-term cognitive dysfunction after posterior fossa tumor surgery? Is white matter integrity-specifically fractional anisotropy (FA) of the superior cerebellar peduncle (SCP)-a key independent predictor of cognitive outcomes? Researchers will compare children who developed long-term cognitive dysfunction (cases) to those who did not (controls) to see if differences in imaging biomarkers (e.g., SCP FA, fMRI abnormalities), tumor characteristics (e.g., location, volume, histology), treatment factors (e.g., radiotherapy, surgical approach), and demographic variables (e.g., age) are associated with cognitive outcomes.

Participants were not asked to perform any tasks or receive any interventions as part of this study, because it is a retrospective analysis of existing medical records and imaging data. Data collected included:

Preoperative and postoperative brain MRI and DTI scans Tumor pathology and surgical reports Treatment details (e.g., radiation, chemotherapy) Neuropsychological assessment results at 1-year follow-up