Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology

Pediatric neuro-oncology is a critical field of neurosurgery, representing the leading cause of disease-related mortality in children. Despite its rarity, it encompasses over 100 diverse disease entities, which significantly complicates preoperative differential diagnosis and surgical planning. This review examines how artificial intelligence (AI) can address these unmet clinical challenges throughout the perioperative period. Preoperatively, AI-driven radiogenomic models extract pixel-level features to enable non-invasive molecular subtyping, such as predicting BRAF alteration status in pediatric low-grade gliomas. Such insights are vital for determining the extent of resection (EOR) with consideration of availability of targeted therapies. Furthermore, AI facilitates automated tumor segmentation, allowing for meticulous surgical planning and more accurate assessment of surgical risks. Intraoperatively, AI significantly accelerates diagnostic turnaround times, which is essential for real-time decision-making. Emerging technologies, including Oxford Nanopore sequencing with neural network classifiers or stimulated Raman histology, allow for the rapid identification of tumor characteristics in operation time window. These tools directly inform the optimal EOR, particularly in cases like medulloblastoma where molecular subgroups dictate surgical aggressiveness. Additionally, AI integration into intraoperative neurophysiological monitoring enhances the preservation of critical neurological functions. Postoperatively, multimodal deep learning models integrate clinical, imaging, and genomic data to improve prognostic accuracy and standardize response assessment via AI integration. While challenges such as data scarcity and the "black box" nature of algorithms persist, innovative strategies offer potential solutions to AI application. AI serves as a transformative tool for personalized precision management, potentially bridging diagnostic disparities and optimizing clinical outcomes for children with central nervous system tumors.