A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.

Journal: Hepatology (Baltimore, Md.)
Published Date: Jun 24, 2021

Abstract

BACKGROUND AND AIMS: Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response.

Authors

  • Amaro Taylor-Weiner
    Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge.
  • Harsha Pokkalla
    PathAI, Boston, MA.
  • Ling Han
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: hanling@chd.edu.cn.
  • Catherine Jia
    Gilead Sciences, Inc., Foster City, CA.
  • Ryan Huss
    Gilead Sciences, Inc., Foster City, CA.
  • Chuhan Chung
    Gilead Sciences, Inc., Foster City, CA.
  • Hunter Elliott
    PathAI, Boston, MA.
  • Benjamin Glass
    PathAI, Boston, MA.
  • Kishalve Pethia
    PathAI, Boston, MA.
  • Oscar Carrasco-Zevallos
    PathAI, Boston, MA.
  • Chinmay Shukla
    PathAI, Boston, MA.
  • Urmila Khettry
    Lahey Hospital & Medical Center (Emeritus), Burlington, MA.
  • Robert Najarian
    University Gastroenterology, Portsmouth, RI.
  • Ross Taliano
    Warren Alpert Medical School of Brown University, Providence, RI.
  • G Mani Subramanian
    Gilead Sciences, Foster City, CA, USA.
  • Robert P Myers
    Gilead Sciences, Inc., Foster City, CA.
  • Ilan Wapinski
    PathAI, Boston, MA.
  • Aditya Khosla
    Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
  • Murray Resnick
    PathAI, Boston, MA.
  • Michael C Montalto
    PathAI, Boston, MA.
  • Quentin M Anstee
    Translational & Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
  • Vincent Wai-Sun Wong
    Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Michael Trauner
    Division of Gastroenterology and Hepatology, Medical University of Vienna, Vienna, Austria.
  • Eric J Lawitz
    Texas Liver Institute, UT Health San Antonio, San Antonio, TX.
  • Stephen A Harrison
    Pinnacle Clinical Research, San Antonio, TX.
  • Takeshi Okanoue
    Hepatology CenterSaiseikai Suita HospitalSuitaJapan.
  • Manuel Romero-Gomez
    Hospital Universitario Virgen del Rocio, Sevilla, Spain.
  • Zachary Goodman
    Department of Medicine, Inova Fairfax Medical Campus, Falls Church, VA.
  • Rohit Loomba
    From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.).
  • Andrew H Beck
  • Zobair M Younossi
    Department of Medicine, Inova Fairfax Medical Campus, Falls Church, VA.

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