Enhancing efficiency in pediatric brain tumor segmentation using a pathologically diverse single-center clinical dataset.

Background: Brain tumors are the most common solid malignancies in children, encompassing diverse histological, molecular subtypes, and imaging features and outcomes. Pediatric brain tumors (PBTs), including high- and low-grade gliomas (HGG and LGG), medulloblastomas (MB), ependymomas, and rarer forms, pose diagnostic and therapeutic challenges. Deep learning (DL)-based segmentation offers promising tools for tumor delineation, yet its performance across heterogeneous PBT subtypes and MRI protocols remains uncertain. Methods: A retrospective single-center cohort of 174 pediatric patients with HGG, LGG, MB, ependymomas, and other rarer subtypes was used. MRI sequences included T1, T1 post-contrast (T1-C), T2, and FLAIR. Manual annotations were provided for 4 tumor subregions: whole tumor (WT), T2-hyperintensity (T2H), enhancing tumor (ET), and cystic component (CC). A 3D nnU-Net model was trained and tested (121/53 split), with segmentation performance assessed using the Dice similarity coefficient (DSC) and compared against intra- and interrater variability. Results: The model achieved robust performance for WT and T2H (mean DSC: 0.85), comparable to human annotator variability (mean DSC: 0.86). ET segmentation was moderately accurate (mean DSC: 0.75), while CC performance was poor (mean DSC: 0.26). Segmentation accuracy varied by tumor type, MRI sequence combination, and location. Notably, T1, T1-C, and T2 combined produced results nearly equivalent to the full protocol. Conclusions: DL-based segmentation is feasible for PBTs, particularly for T2H and WT. Challenges remain for ET and CC segmentation, highlighting the need for further refinement. These findings support the potential for protocol simplification and automation to enhance volumetric assessment and streamline pediatric neuro-oncology workflows.