AI-RANO WG (Focus & Scope)
The Artificial Intelligence for RANO (AI-RANO) working group (WG) describes an international, multidisciplinary initiative that focuses on advancing the development, benchmarking, and validation of diagnostic, prognostic, and predictive AI-driven imaging biomarkers for central nervous system (CNS) tumors. We always welcome in our WG experts in clinical practice, (bio)medical imaging, machine learning, & data science who are dedicated to our focus. By fostering multi-institutional and interdisciplinary collaborations, we aim to address current challenges in accessing large and diverse clinical & imaging data essential for clinical validation.
Our initiatives include conducting evidence-based reviews of emerging AI biomarkers, providing recommendations for clinical trial criteria, enabling data sharing & analysis, and collaborating with other RANO WGs & professional communities to enhance the role of AI in neuro-oncology.
AI-RANO WG Ongoing & Future Studies
Federated Learning
Recurrent Morphology
BraTS-Path Challenge
Detecting glioma boundaries in radiology scans, without sharing patient data
BraTS-Path Challenge
Recurrent Morphology
BraTS-Path Challenge
Ai assessment of histopathologic glioblastoma sub-regions (in partnership with the RANO RGP WG)
Recurrent Morphology
Recurrent Morphology
Recurrent Morphology
Standardized quantification of morphological features in histopathological samples of recurrent glioma (in partnership with the RANO RGP WG)
Post-radiotherapy GBM
Recurrent Morphology
Recurrent Morphology
Survival prediction for GBM patients post-radiotherapy
AI-RANO WG Featured Publications
AI-RANO - A Review (2024)
AI-RANO - Recommendations (2024)
AI-RANO - Recommendations (2024)
The development, application, & benchmarking of AI tools to improve diagnosis, prognostication, & therapy in neuro-oncology are increasing at a rapid pace. This Policy Review, published in the Lancet Oncology, provides an overview & critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before & after therapy, & differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed. [Link to article]
AI-RANO - Recommendations (2024)
AI-RANO - Recommendations (2024)
AI-RANO - Recommendations (2024)
A burgeoning number of diagnostic, predictive, prognostic, & monitoring AI biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. However, the broad applicability & clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, & validation. This Policy Review, published in the Lancet Oncology, intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in neuro-oncology. We investigate the repeatability, reproducibility, & stability of AI in RANO, as well as the pathway for AI validation with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology. [Link to article]
AI-RAPNO - A Review (2025)
AI-RAPNO - Guidelines (2025)
AI-RAPNO - Guidelines (2025)
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, published in the Lancet Oncology, we highlight key developments, challenges, & priority areas for imaging-based AI for paediatric neuro-oncology. Our goal is to provide a focused overview of current capabilities, unmet needs, and future directions at the intersection of AI and paediatric neuro-oncology. [Link to article]
AI-RAPNO - Guidelines (2025)
AI-RAPNO - Guidelines (2025)
AI-RAPNO - Guidelines (2025)
The RAPNO criteria provide an important framework for evaluating treatment efficacy and tumour progression in clinical studies of paediatric brain tumours. As 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, & regulatory & ethical considerations. To address these barriers, this Policy Review, published in the Lancet Oncology, 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. [Link to article]
AI-RANO WG Chairs
Spyridon Bakas, PhD
Raymond Huang, MD PhD
Raymond Huang, MD PhD
Associate Professor of Pathology
Director, Computational Pathology Division
Indiana University School of Medicine, Indianapolis, IN, USA
Raymond Huang, MD PhD
Raymond Huang, MD PhD
Raymond Huang, MD PhD
Associate Professor of Radiology
Director, Neuroradiology Division
Mass General Brigham
Brigham and Women's Hospital
Boston, MA, USA
AI-RANO WG Members
Mariam S. Aboian, MD PhD
Mariam S. Aboian, MD PhD
Mariam S. Aboian, MD PhD
Assistant Professor & Attending Radiologist
Children's Hospital of Philadelphia, Philadelphia, PA, USA
Hugo J.W.L. Aerts, PhD
Mariam S. Aboian, MD PhD
Mariam S. Aboian, MD PhD
Professor
Harvard University, Boston, MA, USA
Hamed Akbari, MD PhD
Mariam S. Aboian, MD PhD
Wenya Linda Bi, MD PhD
Assistant Professor of Bioengineering
Santa Clara University, Santa Clara, CA, USA.
Wenya Linda Bi, MD PhD
Wenya Linda Bi, MD PhD
Wenya Linda Bi, MD PhD
Associate Professor and Neurosurgeon
Brigham and Women's Hospital, Boston, MA, USA.
Thomas C Booth, MD PhD
Wenya Linda Bi, MD PhD
Thomas C Booth, MD PhD
Associate Professor and Neuroradiologist
King's College London, London, UK.
Evan Calabrese, MD PhD
Wenya Linda Bi, MD PhD
Thomas C Booth, MD PhD
Assistant Professor and Neuroradiologist
Duke University Medical Center, Durham, NC, USA
Rivka R Colen
Christos Davatzikos, PhD
Christos Davatzikos, PhD
Associate Professor and Neuroradiologist
University of Pittsburg Medical Center, Pittsburgh, PA, USA.
Christos Davatzikos, PhD
Christos Davatzikos, PhD
Christos Davatzikos, PhD
Professor
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Norbert Galldiks , MD
Christos Davatzikos, PhD
Norbert Galldiks , MD
Professor and Neurologist
University Hospital Cologne & Research Center Juelich, Germany
Rajan Jain, MD
Anahita Fathi Kazerooni, PhD
Norbert Galldiks , MD
Professor and Neuroradiologist
New York University Grossman School of Medicine, New York, NY, USA
Anahita Fathi Kazerooni, PhD
Anahita Fathi Kazerooni, PhD
Anahita Fathi Kazerooni, PhD
Assistant Professor
Children's Hospital of Philadelphia, Philadelphia, PA, USA
Philipp Lohmann, PhD
Anahita Fathi Kazerooni, PhD
Anahita Fathi Kazerooni, PhD
Associate Professor
Research Center Juelich & RWTH Aachen University Hospital, Germany
Janine M. Lupo, PhD
Martha Nowosielski, PhD
Janine M. Lupo, PhD
Associate Professor
UCSF, San Francisco, CA, USA.
Ali Nabavizadeh, MD
Martha Nowosielski, PhD
Janine M. Lupo, PhD
Associate Professor and Radiologist
Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Martha Nowosielski, PhD
Martha Nowosielski, PhD
Martha Nowosielski, PhD
Assistant Professor
University of Heidelberg Medical Center, Heidelberg, Germany
Ghulam Rasool, PhD
Javier Villanueva-Meyer, MD
Martha Nowosielski, PhD
Assistant Professor
Moffitt Cancer Center, Tampa, FL, USA
Pallavi Tiwari, PhD
Javier Villanueva-Meyer, MD
Javier Villanueva-Meyer, MD
Associate Professor
University of Wisconsin-Madison, Madison, WI, USA
Javier Villanueva-Meyer, MD
Javier Villanueva-Meyer, MD
Javier Villanueva-Meyer, MD
Associate Professor and Neuroradiologist
UCSF, San Francisco, CA, USA
Michael Vogelbaum
Benedict Wiestler, MD PhD
Philipp Vollmuth, MD MBA
Our leadership development programs are d
Philipp Vollmuth, MD MBA
Benedict Wiestler, MD PhD
Philipp Vollmuth, MD MBA
Professor and Neuroradiologist
University of Bonn, Bonn, Germany
Benedict Wiestler, MD PhD
Benedict Wiestler, MD PhD
Benedict Wiestler, MD PhD
Professor and Neuroradiologist
Technical University of Munich, Munich, Germany
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