Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

Journal: Journal of vascular and interventional radiology : JVIR
Published Date: Jun 1, 2018

Abstract

PURPOSE: To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques.

Authors

  • Aaron Abajian
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520.
  • Nikitha Murali
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520.
  • Lynn Jeanette Savic
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520; Department of Diagnostic and Interventional Radiology, Universitätsmedizin Charité Berlin, Berlin, Germany.
  • Fabian Max Laage-Gaupp
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520.
  • Nariman Nezami
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520.
  • James S Duncan
    Biomedical Engineering, Yale University, New Haven, CT 06511, USA.
  • Todd Schlachter
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520.
  • MingDe Lin
    Philips Research North America, Cambridge, Massachusetts.
  • Jean-François Geschwind
    Prescience Labs, Westport, Connecticut.
  • Julius Chapiro
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520. Electronic address: julius.chapiro@yale.edu.

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