Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium.

Journal: International journal of radiation biology
Published Date: Feb 6, 2023

Abstract

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67 Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.

Authors

  • Lydia J Wilson
    Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA.
  • Frederico C Kiffer
    Department of Anesthesia and Critical Care Medicine, The Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA.
  • Daniel C Berrios
    USRA/NASA Ames Research Center, Building N-260, Moffett Field, CA 94305, USA.
  • Abigail Bryce-Atkinson
    Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
  • Sylvain V Costes
    NASA Ames Research Center, Moffett Field, CA, USA.
  • Olivier Gevaert
    Department of Biomedical Data Science, Stanford University, CA, 94305, USA.
  • Bruno F E Matarèse
    The Cavendish Laboratory, University of Cambridge, Cambridge, UK.
  • Jack Miller
    NASA Ames Research Center, Moffett Field, CA, USA.
  • Pritam Mukherjee
    Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA. pritam.mukherjee@nih.gov.
  • Kristen Peach
    Department of Bionetics, NASA Ames Research Center, Moffett Field, CA, USA.
  • Paul N Schofield
    Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK. pns12@cam.ac.uk.
  • Luke T Slater
    College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK. l.slater.1@bham.ac.uk.
  • Britta Langen
    Department of Radiation Oncology, Section of Molecular Radiation Biology, UT Southwestern Medical Center, Dallas, TX, USA.

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