• 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, and Response Assessment in Neuro Oncology (RANO) group.
• 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: spbakas@iu.edu.
• Lancet Oncol. 2024 Nov 1; 25 (11): e589e601e589-e601.

AbstractTechnological 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.Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.

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