학술논문
Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology.
Document Type
Academic Journal
Author
Bitterman DS; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.; Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA.; Gensheimer MF; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA.; Jaffray D; Department of Radiation Physics, M.D. Anderson Cancer Center, Houston, TX.; Pryma DA; Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.; Jiang SB; Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX.; Morin O; Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA.; Ginart JB; Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA.; Upadhaya T; Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA.; Vallis KA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.; Buatti JM; Department of Oncology, University of Oxford, Oxford, United Kingdom.; Deasy J; Department of Radiation Oncology, University of Iowa Carver College of Medicine, Iowa City, IA.; Hsiao HT; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY.; Chung C; Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA.; Fuller CD; Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA.; Greenspan E; Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX.; Cloyd-Warwick K; Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD.; Courdy S; DNAnexus, Mountain View, CA.; Mao A; DNAnexus, Mountain View, CA.; Barnholtz-Sloan J; Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX.; Center for Informatics, Digital Vertical, City of Hope National Comprehensive Cancer Center, Los Angeles, CA.; Topaloglu U; Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX.; Hands I; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD.; Cancer Research Informatics Shared Resource Facility, University of Kentucky Markey Cancer Center, Lexington, NY.; Maurer I; Kentucky Cancer Registry, Lexington, NY.; Terry M; GenomOncology, Cleveland, OH.; Curran WJ; MITRE Corporation, Bedford, MA.; GenesisCare, Fort Myers, FL.; Le QT; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA.; Nadaf S; Department of Radiation Oncology, Emory University, Atlanta, GA.; Kibbe W; Cancer Center Informatics Society, Los Angeles, CA.
Source
Publisher: American Society of Clinical Oncology Country of Publication: United States NLM ID: 101708809 Publication Model: Print Cited Medium: Internet ISSN: 2473-4276 (Electronic) Linking ISSN: 24734276 NLM ISO Abbreviation: JCO Clin Cancer Inform Subsets: MEDLINE
Subject
Language
English
Abstract
In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.