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A new method to screen high-risk COPD populations: machine learning-based cascade classification models based on low-dose CT scan

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Abstract

Purpose

To explore the feasibility of machine learning-based cascade classification models for screening high-risk COPD populations.

Materials and methods

A total of 1637 community residents with available demographic data, smoking history, and pulmonary function tests (PFT) who underwent low-dose chest computed tomography (CT) from 2018 to 2020 were included. All subjects were divided into COPD and non-COPD groups according to their FEV1/FVC threshold of 0.7. Furthermore, the non-COPD groups were further subdivided into normal and high-risk COPD groups subgroups according to FEV1% predicted value (FEV1% pre) thresholds of 72%, 80%, and 95%, respectively. Based on the basic information and CT quantitative parameters of subjects, random forest model 1 (RF_1) was established to distinguish COPD from non-COPD groups, and RF_2 was established to distinguish high-risk COPD from normal groups. Then, we combined RF_1 and RF_2 to form triple classification model using cascade classification method. Subjects were randomly divided into training and test sets in the ratio of 8:2. Model performances were evaluated using AUC, accuracy, sensitivity, and specificity.

Results

The accuracy of the triple classification model was 0.63 for FEV1/FVC threshold of 0.7 and FEV1% threshold of 72%. For FEV1/FVC threshold of 0.7 and FEV1% threshold of 80%, accuracy of the model was 0.51. For FEV1/FVC threshold of 0.7 and FEV1% threshold of 95%, accuracy of the model was 0.58.

Conclusions

Machine learning-based cascade classification models is a potential method to screen high-risk COPD populations from general population. This method lays a foundation for a uniform method to screen high-risk COPD populations.

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Data availability

The data sets supporting the conclusion of this article are included within the article. All data are available from the corresponding author upon reasonable request.

Abbreviations

AI:

Artificial intelligence

AUC:

Area under curve

COPD:

Chronic obstructive pulmonary disease

FEV1% pre:

FEV1% predicted values

FEV1:

Forced expiratory volume in 1 s

FVC:

Forced vital capacity

FEV1/FVC:

The ratio of forced expiratory volume in 1 s to forced vital capacity

FOV:

Field of view

fSAD:

Functional small airway disease

PFT:

Pulmonary function test

PRM:

Parameter response mapping

TBV:

Total blood volume

RF:

Random forest

No. vessels:

Number of small pulmonary vessels

No. vessels CSA < 5 mm2 :

Numbers of pulmonary vessels with a cross-sectional area less than 5 mm2

Vessel area:

Area of small pulmonary vessels

Total vessel surface area:

Total surface area of small pulmonary vessels

LV:

Lung volume

TBV:

Total blood volume

BV5:

Vessel volume for vessels less than 5 mm2

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Acknowledgements

We greatly appreciate Ms. Qian He (Department of Statistics, Naval Medical University, Shanghai, China) for her assistance in statistical analysis.

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers: 82171926, 81930049); National Key R&D Program of China (grant number: 2022YFC2010000, 2022YFC2010002, 2022YFC2010005); the program of Science and Technology Commission of Shanghai Municipality (grant number 21DZ2202600); the construction of CT standardized database for chronic obstructive pulmonary disease (grant number: YXFSC2022JJSJ002); Clinical Innovation Project of Shanghai Changzheng Hospital (grant number: 2020YLCYJ-Y24).

Author information

Author notes

  1. Yu Pu, Xiuxiu Zhou, and Di Zhang have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, 415 Fengyang Rd., Shanghai, 200003, China

    Yu Pu, Xiuxiu Zhou, Di Zhang, Yu Guan, Yi Xia, Shiyuan Liu & Li Fan

  2. Shanghai Aitrox Technology Corporation Limited, Shanghai, China

    Yang Lu, Xuebin Zheng & Chuan He

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  1. Yu Pu
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Contributions

YP: conceptualization, formal analysis, data curation, writing—original draft, writing—review and editing, visualization. XZ: conceptualization, formal analysis, data curation, writing—original draft, writing—review and editing, visualization. DZ: methodology, formal analysis, investigation, writing—original draft, visualization. YG: investigation, resources, visualization, supervision. YX: validation, formal analysis. YL: methodology, software, validation, formal analysis. XZ: software, validation, formal analysis. CH: supervision, project administration. SL: resources, supervision, project administration, funding acquisition. LF: conceptualization, methodology, writing—review and editing, supervision, project administration, funding acquisition.

Corresponding authors

Correspondence to Shiyuan Liu or Li Fan.

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Conflict of interest

The authors declare that they have no competing of interest.

Ethical approval

The study was approved by the institutional review board of Changzheng Hospital, Naval Medical University, Shanghai, China, and the study was registered in the Chinese Clinical Trials Registry (http://www.chictr.org.cn/index.aspx; ChiCTR2000035283). The study was conducted in accordance with the Declaration of Helsinki. All the subjects signed written informed consent for participating in this study.

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Pu, Y., Zhou, X., Zhang, D. et al. A new method to screen high-risk COPD populations: machine learning-based cascade classification models based on low-dose CT scan. Chin J Acad Radiol 7, 28–39 (2024). https://doi.org/10.1007/s42058-023-00134-9

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