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CT-based whole lung radiomics nomogram: a tool for identifying the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease

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Abstract

Objectives

To evaluate the value of CT-based whole lung radiomics nomogram for identifying the risk of cardiovascular disease (CVD) in patients with chronic obstructive pulmonary disease (COPD).

Materials and methods

A total of 974 patients with COPD were divided into a training cohort (n = 402), an internal validation cohort (n = 172), and an external validation cohort (n = 400) from three hospitals. Clinical data and CT findings were analyzed. Radiomics features of whole lung were extracted from the non-contrast chest CT images. A radiomics signature was constructed with algorithms. Combined with the radiomics score and independent clinical factors, multivariate logistic regression analysis was used to establish a radiomics nomogram. ROC curve was used to analyze the prediction performance of the model.

Results

Age, weight, and GOLD were the independent clinical factors. A total of 1218 features were extracted and reduced to 15 features to build the radiomics signature. In the training cohort, the combined model (area under the curve [AUC], 0.731) showed better discrimination capability (p < 0.001) than the clinical factors model (AUC, 0.605). In the internal validation cohort, the combined model (AUC, 0.727) performed better (p = 0.032) than the clinical factors model (AUC, 0.629). In the external validation cohort, the combined model (AUC, 0.725) performed better (p < 0.001) than the clinical factors model (AUC, 0.690). Decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model.

Conclusion

The CT-based whole lung radiomics nomogram has the potential to identify the risk of CVD in patients with COPD.

Clinical relevance statement

This study helps to identify cardiovascular disease risk in patients with chronic obstructive pulmonary disease on chest CT scans.

Key Points

• To investigate the value of CT-based whole lung radiomics features in identifying the risk of cardiovascular disease in chronic obstructive pulmonary disease patients.

• The radiomics nomogram showed better performance than the clinical factors model to identify the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease.

• The radiomics nomogram demonstrated excellent performance in the training, internal validation, and external validation cohort (AUC, 0.731; AUC, 0.727; AUC, 0.725).

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Data sharing

Data generated or analyzed during the study are available from the corresponding author by request.

Abbreviations

AECOPD:

Acute exacerbation of chronic obstructive pulmonary disease

AUC:

Area under the ROC curve

CI:

Confidence interval

COPD:

Chronic obstructive pulmonary disease

CT:

Computed tomography

CVD:

Cardiovascular disease

DCA:

Decision curve analysis

FEV1%:

Percent predicted of forced expiratory flow in 1 s

FEV1:

Forced expiratory volume in 1 s

ICD:

International Classification of Disease

mRMR:

Maximal redundancy minimal relevance

NPV:

Negative predictive value

PPV:

Positive predictive value

PRISm:

Preserved ratio impaired spirometry

ROC:

Receiver operating characteristic

VRF:

Vascular risk factors

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Funding

This study has received funding by the National Key R&D Program of China (2022YFC2010002, 2022YFC2010000); the National Natural Science Foundation of China (82171926, 81930049, 81871321); the Medical Imaging Database Construction Program of National Health Commission (YXFSC2022JJSJ002); the Clinical Innovative Project of Shanghai Changzheng Hospital (2020YLCYJ-Y24); and the program of Science and Technology Commission of Shanghai Municipality (21DZ2202600).

Author information

Authors and Affiliations

  1. Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China

    XiaoQing Lin, TaoHu Zhou, Jie Li, Yu Guan, Xin’ang Jiang, Xiuxiu Zhou, Yi Xia, Shiyuan Liu & Li Fan

  2. College of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200003, China

    XiaoQing Lin & Jie Li

  3. School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China

    TaoHu Zhou

  4. Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China

    Jiong Ni

  5. Department of Radiology, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Zhejiang, China

    Fangyi Xu & Hongjie Hu

  6. Department of Radiology, University of Michigan Taubman Center, Ann Arbor, MI, USA

    Qian Dong

Authors
  1. XiaoQing Lin
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  2. TaoHu Zhou
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  3. Jiong Ni
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  7. Xiuxiu Zhou
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  11. Qian Dong
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  12. Shiyuan Liu
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  13. Li Fan
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Corresponding author

Correspondence to Li Fan.

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Guarantor

The scientific guarantor of this publication is Li Fan.

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.

Statistics and biometry

One of the authors (TaoHu Zhou) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained (2022SL068).

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• retrospective

• observational

• multicenter study

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XiaoQing Lin and TaoHu Zhou contributed equally to this work.

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Lin, X., Zhou, T., Ni, J. et al. CT-based whole lung radiomics nomogram: a tool for identifying the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease. Eur Radiol (2024). https://doi.org/10.1007/s00330-023-10502-9

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  • DOI: https://doi.org/10.1007/s00330-023-10502-9

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