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Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach

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A Correction to this article was published on 23 March 2022

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Abstract

Objectives

Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD.

Methods

A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients.

Results

The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale.

Conclusions

The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging.

Key Points

• Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries.

• The proposed chest computed tomography (CT)–based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale.

• The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.

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Abbreviations

%LAA-950:

The percentage of lung volume less than or equal to −950 Hounsfield units

95% CI:

95% confidence interval

AI:

Artificial intelligence

AUC:

Area under the receiver operating characteristic curve

BMI:

Body mass index

COPD:

Chronic obstructive pulmonary disease

CT:

Computed tomography

DL:

Deep learning

FEV1:

Forced expiratory volume in 1 second

FVC:

Forced vital capacity

IQR:

Interquartile range

LDCT:

Low-dose computed tomography

MIL:

Multi-instance learning

NA:

Not applicable

NLST:

National Lung Screening Trial

NPV:

Negative predictive value

PPV:

Positive predictive value

SD:

Standard deviation

Yrs:

Years

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Acknowledgements

We thank LetPub (www.letpub.com) for linguistic assistance and pre-submission expert review.

Funding

This work was supported by the National Key R&D Program (2018YFC1313700), the National Natural Science Foundation of China (grant nos. 82100089, 81870064, and 82070086), and the “Gaoyuan” project of Pudong Health and Family Planning Commission (PWYgy2018-06).

Author information

Author notes

  1. Jiaxing Sun, Ximing Liao and Yusheng Yan contributed equally to this work.

Authors and Affiliations

  1. Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China

    Jiaxing Sun, Ximing Liao, Qian Guo, Shaoyong Gao, Kun Wang & Qiang Li

  2. Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China

    Jiaxing Sun

  3. Department of Pulmonary and Critical Care Medicine, Changsha First Hospital, Changsha, China

    Yusheng Yan

  4. Department of Pulmonary and Critical Care Medicine, People’s Liberation Army Joint Logistic Support Force 920th Hospital, Kunming, Yunnan, China

    Xin Zhang

  5. Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital, Jinan, China

    Jian Sun

  6. Infervision, Beijing, China

    Weixiong Tan, Baiyun Liu & Jiangfen Wu

  7. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China

    Zhang Li

Authors
  1. Jiaxing Sun
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Corresponding authors

Correspondence to Kun Wang or Qiang Li.

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Guarantor

The scientific guarantor of this publication is professor Qiang Li, the head of the Department of Pulmonary and Critical Care Medicine of Shanghai East Hospital, Tongji University School of Medicine.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Informed consent

The requirement for written informed consent was waived due to the retrospective nature of the study.

Ethical approval

This study was approved by the ethics commissions of all participating hospitals, including the Affiliated Hospital of Qingdao University, Changsha First Hospital, People’s Liberation Army Joint Logistic Support Force 920th Hospital, and Shandong Provincial Hospital.

Methodology

• retrospective

• diagnostic or prognostic study

• multi-center study

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The original online version of this article was revised: in this article only the authors Jiaxing Sun and Yusheng Yan were initially listed as having equally contributed to this work. This has been corrected to include the author Ximing Liao, as all three have contributed equally to the data collection, analyzing and drafting of the manuscript.

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Sun, J., Liao, X., Yan, Y. et al. Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach. Eur Radiol 32, 5319–5329 (2022). https://doi.org/10.1007/s00330-022-08632-7

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  • DOI: https://doi.org/10.1007/s00330-022-08632-7

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