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

Lydia J Wilson; Frederico C Kiffer; Daniel C Berrios; Abigail Bryce-Atkinson; Sylvain V Costes; Olivier Gevaert; Bruno F E Matarèse; Jack Miller; Pritam Mukherjee; Kristen Peach; Paul N Schofield; Luke T Slater; Britta Langen

doi:10.1080/09553002.2023.2173823

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

Int J Radiat Biol. 2023;99(8):1291-1300. doi: 10.1080/09553002.2023.2173823. Epub 2023 Feb 6.

Authors

Lydia J Wilson¹, Frederico C Kiffer², Daniel C Berrios³, Abigail Bryce-Atkinson⁴, Sylvain V Costes³, Olivier Gevaert^{5

6}, Bruno F E Matarèse^{7

8}, Jack Miller^{3

9}, Pritam Mukherjee^{5

10}, Kristen Peach¹¹, Paul N Schofield¹², Luke T Slater^{13

14

15}, Britta Langen¹⁶

Affiliations

¹ Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA.
² Department of Anesthesia and Critical Care Medicine, The Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA.
³ NASA Ames Research Center, Moffett Field, CA, USA.
⁴ Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
⁵ Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford, CA, USA.
⁶ Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA.
⁷ The Cavendish Laboratory, University of Cambridge, Cambridge, UK.
⁸ Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
⁹ KBR, NASA Ames Research Center, Moffett Field, CA, USA.
¹⁰ Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD, USA.
¹¹ Department of Bionetics, NASA Ames Research Center, Moffett Field, CA, USA.
¹² Department of Physiology Development and Neuroscience, University of Cambridge, Cambridge, UK.
¹³ Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
¹⁴ Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, UK.
¹⁵ MRC Health Data Research UK (HDR UK), Midlands, UK.
¹⁶ Department of Radiation Oncology, Section of Molecular Radiation Biology, UT Southwestern Medical Center, Dallas, TX, USA.

PMID: 36735963
DOI: 10.1080/09553002.2023.2173823

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^th 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.

Keywords: Machine learning; artificial intelligence; lung cancer; ontology; radiobiology; radiotherapy; voxel-based analysis.

Publication types

Congress
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Artificial Intelligence*
Big Data
Child
Data Mining
Humans
Lung Neoplasms*