Lung cancer prediction by Deep Learning to identify benign lung nodules.

Journal: Lung cancer (Amsterdam, Netherlands)
Published Date: Apr 1, 2021

Abstract

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity.

Authors

  • Marjolein A Heuvelmans
    University Medical Center Groningen, Department of Epidemiology, University of Groningen, 9700 RB Groningen, Netherlands; Department of Pulmonology, Medisch Spectrum Twente, Enschede, Netherlands. Electronic address: m.a.heuvelmans@umcg.nl.
  • Peter M A van Ooijen
    University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
  • Sarim Ather
    Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
  • Carlos Francisco Silva
    Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center, Member of the German Lung Research Center, Heidelberg, Germany; Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany. Electronic address: Carlos.DaSilva@med.uni-heidelberg.de.
  • Daiwei Han
    University Medical Center Groningen, Department of Radiology, University of Groningen, Groningen, The Netherlands.
  • Claus Peter Heussel
    Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center, Member of the German Lung Research Center, Heidelberg, Germany; Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany. Electronic address: heussel_elsevier2019@contbay.com.
  • William Hickes
    Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
  • Hans-Ulrich Kauczor
    Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
  • Petr Novotny
    Respiratory Medicine, Glenfield General Hospital, Leicester, UK.
  • Heiko Peschl
    Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
  • Mieneke Rook
    University Medical Center Groningen, Department of Radiology, University of Groningen, Groningen, The Netherlands.
  • Roman Rubtsov
    Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center, Member of the German Lung Research Center, Heidelberg, Germany; Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany. Electronic address: roman.rubtsov.med@gmail.com.
  • Oyunbileg von Stackelberg
    Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center, Member of the German Lung Research Center, Heidelberg, Germany; Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany. Electronic address: Oyunbileg.Stackelberg@med.uni-heidelberg.de.
  • Maria T Tsakok
    Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address: mariatsakok@ouh.nhs.uk.
  • Carlos Arteta
  • Jérôme Declerck
    Siemens Molecular Imaging, Oxford, UK.
  • Timor Kadir
    Mirada Medical, Oxford, UK.
  • Lyndsey Pickup
    Optellum Ltd, Oxford, UK.
  • Fergus Gleeson
    Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
  • Matthijs Oudkerk
    University Medical Center, Groningen, The Netherlands.

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