@article{2d88d91f1ddd4d9bbdcf99b65a5a0578,
title = "Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice",
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.",
keywords = "MGMT PROMOTER METHYLATION, CENTRAL-NERVOUS-SYSTEM, BRAIN-TUMORS, PATTERN-ANALYSIS, GLIOBLASTOMA, SURVIVAL, FEATURES, GLIOMA, MRI, CLASSIFICATION",
author = "Spyridon Bakas and Philipp Vollmuth and Norbert Galldiks and Booth, {Thomas C.} and Aerts, {Hugo J.W.L.} and Bi, {Wenya Linda} and Benedikt Wiestler and Pallavi Tiwari and Sarthak Pati and Ujjwal Baid and Evan Calabrese and Philipp Lohmann and Martha Nowosielski and Rajan Jain and Rivka Colen and Marwa Ismail and Ghulam Rasool and Lupo, {Janine M.} and Hamed Akbari and Tonn, {Joerg C.} and David Macdonald and Michael Vogelbaum and Chang, {Susan M.} and Christos Davatzikos and Villanueva-Meyer, {Javier E.} and Huang, {Raymond Y.} and {Response Assessment in Neuro Oncology (RANO) group}",
note = "Funding Information: The work presented in this study was partly funded by the National Institutes of Health (NIH) under award numbers: NCI/ITCR: U01CA242871 (to SB), NCI/ITCR: U24CA189523 (to CD), NCI/ITCR: U01CA248226 (to PT), NCI: R01CA264017 (to PT), NCI: U01CA248226 (to PT), NCI: P01CA118816 (to JML), NCI: U24CA194354 (to HJWLA), NCI: U01CA190234 (to HJWLA), NCI: U01CA209414 (to HJWLA), NCI: R35CA22052 (to HJWLA), NCI: U54CA27451 (to HJWLA), NINDS: R01NS042645 (to CD), NCATS: UL1TR001878 (to SB and CD), NCI: CCSG and HN SPORE (to RC). It was also partly funded by the Veterans Affairs (VA) Merit Award: 1I01BX005842\u201301A2 (to PT), the DOD/PRCRP Career Development Award: W81XWH-18\u20131-0404 (to PT), the Dana Foundation David Mahoney Neuroimaging Program (to PT), the V Foundation Translational Research Award (to PT), the Johnson & Johnson WiSTEM2D Award (to PT), the Ohio Third Frontier Technology Validation Fund (to PT), the Musella Foundation Grant (to MI and PT), the R&D Pilot Award, Departments of Radiology and Medical Physics, University of Wisconsin-Madison (to MI and PT), the European Union: European Research Council (866504; to HJWLA), the Deutsche Forschungsgemeinschaft (project number 428090865/SPP 2177; to PL and NG), the Medical Research Council: MR/W021684/1 (to TCB), and the Wellcome/Engineering and Physical Sciences Research Council Centre for Medical Engineering (WT 203,148/Z/16/Z; to TCB). The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH or of any other funding body. SB, SP, UB, and HA conducted part of the work reported in this manuscript at their current affiliation, as well as while they were affiliated with the Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D), the Center for Biomedical Image Computing and Analytics (CBICA), and the Department of Radiology, Perelman School of Medicine, at the University of Pennsylvania. Publisher Copyright: {\textcopyright} 2024 Elsevier Ltd",
year = "2024",
month = nov,
day = "1",
doi = "10.1016/S1470-2045(24)00315-2",
language = "English",
volume = "25",
pages = "e589--e601",
journal = "The Lancet Oncology",
issn = "1470-2045",
publisher = "Elsevier Ltd",
number = "11",
}