Artificial Intelligence for Radiation Oncology Applications Using Public Datasets
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A2 Katsausartikkeli tieteellisessä aikakauslehdessä
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2022-10
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en
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Seminars in Radiation Oncology, Volume 32, issue 4, pp. 400-414
Abstract
Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.Description
Funding Information: This work was supported by the National Institutes of Health (NIH)/National Cancer Institute (NCI) through a Cancer Center Support Grant ( CCSG; P30CA016672-44 ). K.A.W. is supported by the Dr. John J. Kopchick Fellowship through The University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, the American Legion Auxiliary Fellowship in Cancer Research, and an NIH/National Institute for Dental and Craniofacial Research (NIDCR) F31 fellowship ( 1 F31DE031502-01 ). C.D.F. received funding from the NIH/NIDCR ( 1R01DE025248-01/R56DE025248 ); an NIH/NIDCR Academic-Industrial Partnership Award ( R01DE028290 ); the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant ( NSF 1557679) ; the NIH Big Data to Knowledge (BD2K) Program of the NCI Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award ( 1R01CA214825 ); the NCI Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program ( 1R01CA218148 ); an NIH/NCI Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program ( P30CA016672 ); an NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award ( P50CA097007 ); and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Program ( R25EB025787 ). Publisher Copyright: © 2022 The Author(s)
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Wahid, K A, Glerean, E, Sahlsten, J, Jaskari, J, Kaski, K, Naser, M A, He, R, Mohamed, A S R & Fuller, C D 2022, ' Artificial Intelligence for Radiation Oncology Applications Using Public Datasets ', Seminars in Radiation Oncology, vol. 32, no. 4, pp. 400-414 . https://doi.org/10.1016/j.semradonc.2022.06.009