Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.

Journal: European radiology
Published Date: Feb 8, 2021

Abstract

OBJECTIVES: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size.

Authors

  • Sohee Park
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, College of Medicine, University of Ulsan, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138736, South Korea.
  • Gwangbeen Park
    VUNO Inc., Seoul, South Korea.
  • Sang Min Lee
    Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Wooil Kim
    From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.).
  • Hyunho Park
    VUNO, Seoul, Korea.
  • Kyuhwan Jung
    VUNO Inc., Seoul, South Korea.
  • Joon Beom Seo
    Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.

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