Artificial Intelligence for Response Assessment in Pediatric Neuro-Oncology (AI-RAPNO), part 1: review of the current state of the art - 28/10/25

Doi : 10.1016/S1470-2045(25)00484-X

Benjamin H Kann, MD ^a,^b,^c,^d,⁎ , Arastoo Vossough, ProfMD PhD ^e,^f,^p,^w, Sarah C Brüningk, PhD ^h, Ariana M Familiar, PhD ^f,^g, Mariam Aboian, MD ^e,^p, Marius George Linguraru, ProfPhD ^i,^j, Kristen W Yeom, MD ^k, Susan M Chang, ProfMD ^l, Darren Hargrave, ProfMD ^m, David Mirsky, MD ⁿ, Phillip B Storm, ProfMD ^f,^g,^o, Raymond Y Huang, MD ^q, Adam C Resnick, ProfPhD ^f,^g,^o, Michael Weller, ProfMD ^r, Sabine Mueller, ProfMD ^s, Michael Prados, ProfMD ^s, Andrew C Peet, ProfPhD ^t,^u, Javier E Villanueva-Meyer, MD ^v, Spyridon Bakas, PhD ^w,^x,^y,^z, Jason Fangusaro, ProfMD ^aa, Ali Nabavizadeh, MD ^f,^p,^{*
Contributed equally}, Anahita Fathi Kazerooni, PhD ^f,^g,^o,^{*
Contributed equally}
for the
Response Assessment in Pediatric Neuro-Oncology (RAPNO) group

^a Department of Radiation Oncology, Brigham and Women’s Hospital, Boston, MA, USA

^b Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

^c Artificial Intelligence in Medicine Program, Mass General Brigham, Boston, MA, USA

^d Boston Children’s Hospital, Boston, MA, USA

^e Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

^f Center for Data-Driven Discovery in Biomedicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

^g Department of Neurosurgery, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

^h Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland

ⁱ Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington DC, USA

^j School of Medicine and Health Sciences, George Washington University, Washington, DC, USA

^k Division of Neuroradiology, Department of Radiology, Phoenix Children’s Hospital, AZ, USA

^l Division of Neuro-Oncology, Department of Neurosurgery, University of California, San Francisco, CA, USA

^m Great Ormond Street Institute of Child Health, University College London, London, UK

ⁿ Department of Radiology, Children’s Hospital Colorado, University of Colorado, CO, USA

^o Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

^p Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

^q Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

^r Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland

^s Department of Neurological Surgery and Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA

^t Department of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK

^u Oncology Department, Birmingham Children’s Hospital, Birmingham, UK

^v Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA

^w Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA

^x Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA

^y Department of Computer Science, Luddy School of Informatics, Computing, Engineering, Indiana University, Indianapolis, IN, USA

^z Medical Research Group, MLCommons, San Francisco, CA, USA

^aa The Aflac Cancer Center, Children’s Healthcare of Atlanta and the Emory University School of Medicine, Atlanta, GA, USA

^* Correspondence to: Assoc Prof Benjamin H Kann, Department of Radiation Oncology, Brigham and Women’s Hospital, Boston 02115, MA, USA Department of Radiation Oncology Brigham and Women’s Hospital Boston MA 02115 USA

Summary

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.

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Plan

Introduction

The motivation for AI-RAPNO

AI-RAPNO: clinical use cases and targeted review of the literature

Tumour burden measurement

Differential diagnosis

Prediction of molecular landscape beyond driver mutations

Risk stratification and outcome prediction

Conclusions

Search strategy and selection criteria

Contributors

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Vol 26 - N° 11

P. e597-e606 - novembre 2025 Retour au numéro

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

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Artificial Intelligence for Response Assessment in Pediatric Neuro-Oncology (AI-RAPNO), part 1: review of the current state of the art - 28/10/25

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