Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.

Journal: Medical physics
Published Date: Jun 7, 2019

Abstract

PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment.

Authors

  • Johanna Uthoff
    Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.
  • Matthew J Stephens
    Department of Radiology, University of Cincinnati, Cincinnati, OH, 45267, USA.
  • John D Newell
    Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52240, USA.
  • Eric A Hoffman
  • Jared Larson
    Department of Radiology, University of Iowa, Iowa City, IA, 52242, USA.
  • Nicholas Koehn
    Department of Radiology, University of Iowa, Iowa City, IA, 52242, USA.
  • Frank A De Stefano
    Department of Radiology, University of Iowa, Iowa City, IA, 52242, USA.
  • Chrissy M Lusk
    Karmanos Cancer Institute, Wayne State University, Detroit, MI, 48201, USA.
  • Angela S Wenzlaff
    Karmanos Cancer Institute, Wayne State University, Detroit, MI, 48201, USA.
  • Donovan Watza
    Karmanos Cancer Institute, Wayne State University, Detroit, MI, 48201, USA.
  • Christine Neslund-Dudas
    Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, 48202, USA.
  • Laurie L Carr
    Department of Medicine, National Jewish Health, Denver, CO, 80206, USA.
  • David A Lynch
    National Jewish Health, Denver, CO, USA.
  • Ann G Schwartz
    Karmanos Cancer Institute, Wayne State University, Detroit, MI, 48201, USA.
  • Jessica C Sieren
    Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.

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