Radiomics and Deep Learning: Hepatic Applications.

Journal: Korean journal of radiology
Published Date: Apr 1, 2020

Abstract

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

Authors

  • Hyo Jung Park
    Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Bumwoo Park
    Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.
  • Seung Soo Lee
    From the Department of Computer Science, Hanyang University, Seoul, Republic of Korea (K.J.C.); Department of Radiology and Research Institute of Radiology (J.K.J., S.S.L., Y.S.S., W.H.S., H.S.K., J.Y., J.H.K., S.Y.K.) and Department of Diagnostic Pathology (E.S.Y.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (J.Y.C.); Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Korea (B.K.K.).

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