Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice

Spyridon Bakas; Philipp Vollmuth; Norbert Galldiks; Thomas C Booth; Hugo J W L Aerts; Wenya Linda Bi; Benedikt Wiestler; Pallavi Tiwari; Sarthak Pati; Ujjwal Baid; Evan Calabrese; Philipp Lohmann; Martha Nowosielski; Rajan Jain; Rivka Colen; Marwa Ismail; Ghulam Rasool; Janine M Lupo; Hamed Akbari; Joerg C Tonn; David Macdonald; Michael Vogelbaum; Susan M Chang; Christos Davatzikos; Javier E Villanueva-Meyer; Raymond Y Huang; Response Assessment in Neuro Oncology (RANO) group

doi:10.1016/S1470-2045(24)00315-2

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice

Lancet Oncol. 2024 Nov;25(11):e589-e601. doi: 10.1016/S1470-2045(24)00315-2.

Authors

Spyridon Bakas¹, Philipp Vollmuth², Norbert Galldiks³, Thomas C Booth⁴, Hugo J W L Aerts⁵, Wenya Linda Bi⁶, Benedikt Wiestler⁷, Pallavi Tiwari⁸, Sarthak Pati⁹, Ujjwal Baid¹⁰, Evan Calabrese¹¹, Philipp Lohmann¹², Martha Nowosielski¹³, Rajan Jain¹⁴, Rivka Colen¹⁵, Marwa Ismail⁸, Ghulam Rasool¹⁶, Janine M Lupo¹⁷, Hamed Akbari¹⁸, Joerg C Tonn¹⁹, David Macdonald²⁰, Michael Vogelbaum²¹, Susan M Chang²², Christos Davatzikos²³, Javier E Villanueva-Meyer¹⁷, Raymond Y Huang²⁴; Response Assessment in Neuro Oncology (RANO) group

Affiliations

¹ Department of Pathology & Laboratory Medicine, Division of Computational Pathology, Indiana University, Indianopolis, IN, USA; Department of Radiology & Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA; Department of Neurological Surgery, School of Medicine, Indiana University, Indianapolis, IN, USA; Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianopolis, IN, USA; Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, IN, USA. Electronic address: [email protected].
² Division for Computational Radiology and Clinical AI, Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany; Faculty of Medicine, University of Bonn, Bonn, Germany; Division for Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
³ Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Institute of Neuroscience and Medicine, Research Center Juelich, Juelich, Germany.
⁴ School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, UK.
⁵ Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, Maastricht University, Maastricht, Netherlands.
⁶ Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁷ Department of Neuroradiology, University Hospital, Technical University of Munich, Munich, Germany.
⁸ Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
⁹ Department of Pathology & Laboratory Medicine, Division of Computational Pathology, Indiana University, Indianopolis, IN, USA.
¹⁰ Department of Pathology & Laboratory Medicine, Division of Computational Pathology, Indiana University, Indianopolis, IN, USA; Department of Radiology & Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianopolis, IN, USA.
¹¹ Department of Radiology, School of Medicine, Duke University, Durham, NC, USA.
¹² Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany.
¹³ Department of Neurology, Medical University Innsbruck, Innsbruck, Austria.
¹⁴ Department of Radiology and Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA.
¹⁵ Department of Radiology, Neuroradiology Division, Center for Artificial Intelligence Innovation in Medical Imaging, University of Pittsburgh, Pittsburgh, PA, USA.
¹⁶ Department of Machine Learning, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
¹⁷ Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
¹⁸ Department of Bioengineering, School of Engineering, Santa Clara University, Santa Clara, CA, USA.
¹⁹ Department of Neurosurgery, Ludwig-Maximilians-University, Munich, Germany; German Cancer Consortium, Partner Site Munich, Munich, Germany.
²⁰ London Regional Cancer Programme, London, ON, Canada.
²¹ Department of Neuro-Oncology, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA; Department of Neurosurgery, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA; H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
²² Department of Neurological Surgery, Division of Neuro-Oncology, University of California San Francisco, San Francisco, CA, USA.
²³ Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Center for Artificial Intelligence for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
²⁴ Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

PMID: 39481415
DOI: 10.1016/S1470-2045(24)00315-2

Abstract

Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed 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.

Publication types

Review

MeSH terms

Artificial Intelligence* / standards
Brain Neoplasms / therapy
Humans
Medical Oncology* / standards
Reproducibility of Results