Artificial Intelligence for Response Assessment in Pediatric Neuro-Oncology (AI-RAPNO), part 1: review of the current state of the art

Authors

• Benjamin H. Kann, Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA, USA; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine Program, Mass General Brigham, Boston, MA, USA; Boston Children's Hospital, Boston, MA, USA. Electronic address: bkann@bwh.harvard.edu.
• Arastoo Vossough, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Data-Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
• Sarah C. Brüningk, Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
• Ariana M. Familiar, Center for Data-Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
• Mariam Aboian, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
• Marius George Linguraru, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA; School of Medicine and Health Sciences, George Washington University, Washington, DC, USA.
• Kristen W. Yeom, Division of Neuroradiology, Department of Radiology, Phoenix Children's Hospital, AZ, USA.
• Susan M. Chang, Division of Neuro-Oncology, Department of Neurosurgery, University of California, San Francisco, CA, USA.
• Darren Hargrave, Great Ormond Street Institute of Child Health, University College London, London, UK.
• David Mirsky, Department of Radiology, Children's Hospital Colorado, University of Colorado, CO, USA.
• Phillip B. Storm, Center for Data-Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
• Raymond Y. Huang, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
• Adam C. Resnick, Center for Data-Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
• Michael Weller, Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland.
• Sabine Mueller, Department of Neurological Surgery and Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
• Michael Prados, Department of Neurological Surgery and Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
• Andrew C. Peet, Department of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Oncology Department, Birmingham Children's Hospital, Birmingham, UK.
• Javier E. Villanueva-Meyer, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
• Spyridon Bakas, Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA; Department of Computer Science, Luddy School of Informatics, Computing, Engineering, Indiana University, Indianapolis, IN, USA; Medical Research Group, MLCommons, San Francisco, CA, USA.
• Jason Fangusaro, The Aflac Cancer Center, Children's Healthcare of Atlanta and the Emory University School of Medicine, Atlanta, GA, USA.
• Ali Nabavizadeh, Center for Data-Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
• Anahita Fathi Kazerooni, Center for Data-Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Document Type

Journal Article

Publication Date

11-1-2025

Journal

The Lancet. Oncology

Volume

26

Issue

11

DOI

10.1016/S1470-2045(25)00484-X

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.

APA Citation

Kann, Benjamin H.; Vossough, Arastoo; Brüningk, Sarah C.; Familiar, Ariana M.; Aboian, Mariam; Linguraru, Marius George; Yeom, Kristen W.; Chang, Susan M.; Hargrave, Darren; Mirsky, David; Storm, Phillip B.; Huang, Raymond Y.; Resnick, Adam C.; Weller, Michael; Mueller, Sabine; Prados, Michael; Peet, Andrew C.; Villanueva-Meyer, Javier E.; Bakas, Spyridon; Fangusaro, Jason; Nabavizadeh, Ali; and Kazerooni, Anahita Fathi, "Artificial Intelligence for Response Assessment in Pediatric Neuro-Oncology (AI-RAPNO), part 1: review of the current state of the art" (2025). GW Authored Works. Paper 8178.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/8178

Department

Pediatrics