AI-RAPNO Review: Current State and Future Directions for AI in Paediatric Neuro-Oncology Imaging
This Lancet Oncology policy review surveys AI applications in paediatric neuro-oncology, covering tumour segmentation, treatment response evaluation, recurrence prediction, and integrative multimodal analysis. The authors highlight that paediatric brain tumours require tailored AI solutions distinct from adult models, and identify data heterogeneity, limited generalisability, and clinical integration as the primary barriers to implementation.
The original study
Artificial Intelligence for Response Assessment in Pediatric Neuro-Oncology (AI-RAPNO), part 1: review of the current state of the art.
- Authors
- Kann BH, Vossough A, Brüningk SC, Familiar AM, Aboian M, Linguraru MG, et al.
- Journal
- The Lancet. Oncology
- Type
- Journal Article, Review
- PMID
- 41167227
Original abstract
Artificial intelligence (AI) has the potential to enable more precise, efficient, and reproducible interpretation of medical imaging data to improve patient care in paediatric neuro-oncology. Paediatric brain tumours present distinct histopathological, molecular, and clinical challenges that require tailored AI solutions. Recent advances have led to paediatric-specific AI tools for tumour segmentation, treatment response evaluation, recurrence prediction, toxicity assessment, and integrative multimodal analysis. These innovations have the potential to improve diagnostic accuracy, streamline workflows, and inform personalised treatment strategies. However, clinical implementation remains hindered by challenges related to data heterogeneity, model generalisability, and integration into clinical practice. In this Policy Review, we highlight key developments, challenges, and priority areas for imaging-based AI for paediatric neuro-oncology. Our goal is to provide oncology practitioners with a focused overview of current capabilities, unmet needs, and future directions at the intersection of AI and paediatric neuro-oncology.