Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

Journal: Abdominal radiology (New York)
PMID:

Abstract

PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.

Authors

  • Daniel Stocker
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Stefanie Hectors
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Brett Marinelli
    Department of Radiology, Mount Sinai Health System, New York, New York.
  • Guillermo Carbonell
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Octavia Bane
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine Mount Sinai, New York, New York, USA.
  • Miriam Hulkower
    Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Paul Kennedy
    School of Software, University of Technology Sydney, 2007, Sydney, Australia.
  • Weiping Ma
    Department of Pharmacology, Qingdao University Medical College, 422 Boya Building, 308 Ningxia Road, Qingdao, Shandong, 266071, China.
  • Sara Lewis
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Edward Kim
    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
  • Pei Wang
    College of Engineering and Technology, Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, Southwest University, Chongqing, China.
  • Bachir Taouli
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. bachir.taouli@mountsinai.org.