Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.

Journal: JNCI cancer spectrum
Published Date: Sep 2, 2024

Abstract

BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients.

Authors

  • Roger Y Kim
    Division of Pulmonary, Allergy and Critical Care, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Clarisa Yee
    NYU Langone Health, New York City, NY, USA.
  • Sana Zeb
    Division of Pulmonary, Allergy and Critical Care, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Jennifer Steltz
    Division of Pulmonary, Allergy and Critical Care, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Andrew J Vickers
    Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Katharine A Rendle
    Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Nandita Mitra
    1 Department of Biostatistics & Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.
  • Lyndsey C Pickup
    Optellum Ltd., Oxford, United Kingdom.
  • David M DiBardino
    Division of Pulmonary, Allergy and Critical Care, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Anil Vachani
    Division of Pulmonary, Allergy and Critical Care Medicine, Thoracic Oncology Group, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; Abramson Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.

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