Convolutional Neural Network Based COPD and Emphysema Classifications Are Predictive of Lung Cancer Diagnosis

  • Charles HattEmail author
  • Craig Galban
  • Wassim Labaki
  • Ella Kazerooni
  • David Lynch
  • Meilan Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)


Lung cancer is a leading cause of mortality and morbidity for patients suffering from Chronic Obstructive Pulmonary Disease (COPD). Both the presence of visually assessed emphysema on CT scans and abnormal pulmonary function tests are associated with the development of lung cancer. Based on recent results showing that convolutional neural networks (CNNs) applied to CT scans can predict spirometrically-defined COPD (\(\frac{FEV_{1}}{FVC}<0.7\)), we hypothesized that CNN-based classification of COPD and emphysema is predictive of lung cancer development in the National Lung Cancer Screening (NLST) cohort. We trained spirometric COPD and visual emphysema CNN classifiers using data from the COPDGene study. The classifiers were then used to generate COPD and emphysema scores (\(CS_{CNN}\) and \(ES_{CNN}\), respectively) on 7347 CT scans from the NLST study. Cox proportional hazards regression was used to model the effects of \(CS_{CNN}\), \(ES_{CNN}\), age, body mass index, education, gender, smoking pack-years, and years since smoking cessation on lung cancer diagnosis. It was found that, individually, both \(CS_{CNN}\) and \(ES_{CNN}\) were statistically significant predictors (p < 0.000 and p < 0.000, respectively) of lung cancer diagnosis hazard.


CNN COPD Lung cancer screening Survival analysis 



The authors thank the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI. This work was supported by NIH grant 2R44CA203050-02. The COPDGene study is supported by NIH Grant Numbers R01 HL089897 and R01 HL089856, and is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, Novartis, Pfizer, Siemens, Sunovion and GlaxoSmithKline.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Charles Hatt
    • 1
    • 2
    Email author
  • Craig Galban
    • 2
  • Wassim Labaki
    • 3
  • Ella Kazerooni
    • 2
    • 3
  • David Lynch
    • 4
  • Meilan Han
    • 3
  1. 1.Imbio LLCAnn ArborUSA
  2. 2.Department of RadiologyUniversity of MichiganAnn ArborUSA
  3. 3.Department of Internal Medicine, Division of Pulmonary and Critical CareUniversity of MichiganAnn ArborUSA
  4. 4.Department of Medicine, Division of RadiologyNational Jewish HealthDenverUSA

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