Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

Journal: The British journal of radiology
PMID: 34520246

Abstract

OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes.

Authors

  • Bino Abel Varghese
    Keck School of Medicine, University of Southern California, CA, USA.
  • Heeseop Shin
    Keck School of Medicine, University of Southern California, CA, USA.
  • Bhushan Desai
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.
  • Ali Gholamrezanezhad
    Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA.
  • Xiaomeng Lei
    Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Melissa Perkins
    Keck School of Medicine, University of Southern California, CA, USA.
  • Assad Oberai
    Department of Aerospace and Mechanical Engineering, Univ. of Southern California, Los Angeles, CA, USA.
  • Neha Nanda
    Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Steven Cen
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.
  • Vinay Duddalwar
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.

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