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Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology.
Bitterman, Danielle S; Gensheimer, Michael F; Jaffray, David; Pryma, Daniel A; Jiang, Steve B; Morin, Olivier; Ginart, Jorge Barrios; Upadhaya, Taman; Vallis, Katherine A; Buatti, John M; Deasy, Joseph; Hsiao, H Timothy; Chung, Caroline; Fuller, Clifton D; Greenspan, Emily; Cloyd-Warwick, Kristy; Courdy, Samir; Mao, Allen; Barnholtz-Sloan, Jill; Topaloglu, Umit; Hands, Isaac; Maurer, Ian; Terry, May; Curran, Walter J; Le, Quynh-Thu; Nadaf, Sorena; Kibbe, Warren.
Afiliação
  • Bitterman DS; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.
  • Gensheimer MF; Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA.
  • Jaffray D; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA.
  • Pryma DA; Department of Radiation Physics, M.D. Anderson Cancer Center, Houston, TX.
  • Jiang SB; Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Morin O; Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX.
  • 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; Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA.
  • Buatti JM; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.
  • Deasy J; Department of Oncology, University of Oxford, Oxford, United Kingdom.
  • Hsiao HT; Department of Radiation Oncology, University of Iowa Carver College of Medicine, Iowa City, IA.
  • Chung C; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY.
  • Fuller CD; Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA.
  • Greenspan E; Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA.
  • Cloyd-Warwick K; Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX.
  • Courdy S; Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD.
  • Mao A; DNAnexus, Mountain View, CA.
  • Barnholtz-Sloan J; DNAnexus, Mountain View, CA.
  • Topaloglu U; Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX.
  • Hands I; Center for Informatics, Digital Vertical, City of Hope National Comprehensive Cancer Center, Los Angeles, CA.
  • Maurer I; Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX.
  • Terry M; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD.
  • Curran WJ; Cancer Research Informatics Shared Resource Facility, University of Kentucky Markey Cancer Center, Lexington, NY.
  • Le QT; Kentucky Cancer Registry, Lexington, NY.
  • Nadaf S; GenomOncology, Cleveland, OH.
  • Kibbe W; . MITRE Corporation, Bedford, MA.
JCO Clin Cancer Inform ; 7: e2300136, 2023 Sep.
Article em En | MEDLINE | ID: mdl-38055914
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radioterapia (Especialidade) / Neoplasias Limite: Humans Idioma: En Revista: JCO Clin Cancer Inform Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Marrocos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radioterapia (Especialidade) / Neoplasias Limite: Humans Idioma: En Revista: JCO Clin Cancer Inform Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Marrocos