Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects.

Journal: European radiology
PMID: 36988714

Abstract

OBJECTIVES: The objective of this study was to translate a deep learning (DL) approach for semiautomated analysis of body composition (BC) measures from standard of care CT images to investigate the prognostic value of BC in pediatric, adolescent, and young adult (AYA) patients with lymphoma.

Authors

  • Nguyen K Tram
    Center for Regenerative Medicine, The Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, WB4133, Columbus, OH, 43215, USA.
  • Ting-Heng Chou
    Center for Regenerative Medicine, The Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, WB4133, Columbus, OH, 43215, USA.
  • Sarah A Janse
    Center for Biostatistics, The Ohio State University, Columbus, OH, USA.
  • Adam J Bobbey
    Department of Radiology, Nationwide Children's Hospital, Columbus, OH, USA.
  • Anthony N Audino
    Division of Hematology/Oncology/BMT, Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA.
  • John A Onofrey
  • Mitchel R Stacy
    Center for Regenerative Medicine, The Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, WB4133, Columbus, OH, 43215, USA. Mitchel.Stacy@NationwideChildrens.org.

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