Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

Journal: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Published Date: Feb 17, 2020

Abstract

BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor () mutations.

Authors

  • Jay Kumar Raghavan Nair
    Department of Radiology, 54473McGill University Health Centre, Montreal, Québec, Canada.
  • Umar Abid Saeed
    Department of Radiology, 54473McGill University Health Centre, Montreal, Québec, Canada.
  • Connor C McDougall
    Department of Mechanical Engineering, 2129University of Calgary, Calgary, Alberta, Canada.
  • Ali Sabri
    Department of Radiology, McMaster University, Hamilton, Ontario, Canada.
  • Bojan Kovacina
    Department of Radiology, Jewish General Hospital, Montreal, Québec, Canada.
  • B V S Raidu
    Raidu Analysts and Associates, Mumbai, India.
  • Riaz Ahmed Khokhar
    Department of Radiology, 54473McGill University Health Centre, Montreal, Québec, Canada.
  • Stephan Probst
    Department of Nuclear Medicine, Jewish General Hospital, Québec, Montreal, Canada.
  • Vera Hirsh
    Department of Oncology, 5620McGill University Health Centre, Montreal, Québec, Canada.
  • Jeffrey Chankowsky
    Department of Radiology, Royal Victoria Hospital, McGill University Health Centre, 1001 Decarie Blvd, Montreal, QC, H4A 3J1, Canada.
  • Léon C Van Kempen
    Department of Pathology, 10173University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
  • Jana Taylor
    Department of Radiology, 54473McGill University Health Centre, Montreal, Québec, Canada.

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