Radiomic 'Stress Test': exploration of a deep learning radiomic model in a high-risk prospective lung nodule cohort.

Journal: BMJ open respiratory research
Published Date: Jun 27, 2025

Abstract

BACKGROUND: Indeterminate pulmonary nodules (IPNs) are commonly biopsied to ascertain a diagnosis of lung cancer, but many are ultimately benign. The Lung Cancer Prediction (LCP) score is a commercially available deep learning radiomic model with strong diagnostic performance in incidentally identified IPNs, but its potential use to reduce the need for invasive procedures has not been evaluated in patients with nodules for which a biopsy has been recommended.

Authors

  • David Xiao
    Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn.
  • Yency Forero
    Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Michael N Kammer
    Vanderbilt University Medical Center, Nashville, TN. Electronic address: Michael.kammer@vumc.org.
  • Heidi Chen
    Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN.
  • Rafael Paez
    Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn.
  • Brent E Heideman
    Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Oreoluwa Owoseeni
    Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Ian Johnson
    Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Stephen A Deppen
    Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Eric L Grogan
    Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn.
  • Fabien Maldonado
    Mechanical Engineering Department, Vanderbilt University, Nashville, TN, USA.

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