Artificial Intelligence for Response Assessment in Pediatric Neuro-Oncology (AI-RAPNO), part 2: challenges, opportunities, and recommendations for clinical translation

Authors

• 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. Electronic address: fathikazea@chop.edu.
• 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.
• Sarah C. Brüningk, Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
• Arastoo Vossough, Center for Data-Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; 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; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, 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.
• Raymond Y. Huang, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
• Darren Hargrave, Great Ormond Street Institute of Child Health, University College London, London, UK.
• Andrew C. Peet, Department of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Oncology Department, Birmingham Children's Hospital, Birmingham, UK.
• 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.
• 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.
• David Mirsky, Department of Radiology, Children's Hospital Colorado, University of Colorado, CO, USA.
• Kristen W. Yeom, Division of Neuroradiology, Department of Radiology, Stanford University, Stanford, CA, USA.
• Michael Weller, Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland.
• Michael Prados, Department of Neurology and Pediatrics, University of California San Francisco, San Francisco, CA, USA.
• Susan M. Chang, Division of Neuro-Oncology, Department of Neurosurgery, University of California, San Francisco, CA, USA.
• Sabine Mueller, Department of Neurology and Pediatrics, University of California San Francisco, San Francisco, CA, USA.
• 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 and 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.
• 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.
• 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.

Document Type

Journal Article

Publication Date

11-1-2025

Journal

The Lancet. Oncology

Volume

26

Issue

11

DOI

10.1016/S1470-2045(25)00489-9

Abstract

The Response Assessment in Pediatric Neuro-Oncology (RAPNO) criteria provide an important framework for evaluating treatment efficacy and tumour progression in clinical studies of paediatric brain tumours. As artificial intelligence (AI) rapidly transforms clinical practice, integrating AI into the RAPNO framework presents a unique opportunity to enhance quantitative, data-driven approaches for response assessment. However, successful clinical implementation faces challenges, including variability in imaging protocols, scarce annotated datasets, and regulatory and ethical considerations. To address these barriers, this Policy Review, led by the AI for Assessment in Pediatric Neuro-Oncology (AI-RAPNO) subcommittee, outlines key challenges and proposes recommendations to improve AI trustworthiness, generalisability, and implementation in paediatric neuro-oncology. We highlight the potential of AI for response assessment, multimodal integration, and synthetic control groups in clinical trials. Our recommendations emphasise the need for standardised imaging protocols, robust validation frameworks, and infrastructure to support AI readiness in clinical studies. By addressing these needs, AI-RAPNO aims to bridge the gap between AI research and clinical application, ensuring reliable and actionable AI-driven tools for paediatric neuro-oncology.

APA Citation

Kazerooni, Anahita Fathi; Familiar, Ariana M.; Aboian, Mariam; Brüningk, Sarah C.; Vossough, Arastoo; Linguraru, Marius George; Huang, Raymond Y.; Hargrave, Darren; Peet, Andrew C.; Resnick, Adam C.; Storm, Phillip B.; Mirsky, David; Yeom, Kristen W.; Weller, Michael; Prados, Michael; Chang, Susan M.; Mueller, Sabine; Villanueva-Meyer, Javier E.; Bakas, Spyridon; Fangusaro, Jason; Kann, Benjamin H.; and Nabavizadeh, Ali, "Artificial Intelligence for Response Assessment in Pediatric Neuro-Oncology (AI-RAPNO), part 2: challenges, opportunities, and recommendations for clinical translation" (2025). GW Authored Works. Paper 8177.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/8177

Department

Pediatrics