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Radiomics approach for survival prediction in chronic obstructive pulmonary disease



To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS).


This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.


The five features remaining after the LASSO analysis were %LAA−950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index.


A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.

Key Points

• A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA −950 , AWT_Pi10_6 th , AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA 18mm were selected to generate a radiomics model.

• A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group.

• Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

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Age, dyspnea, obstruction


Asian Network of Obstructive Lung Disease


Body mass index


Body mass index, obstruction, dyspnea, exercise


Harrell’s concordance index


Chronic obstructive pulmonary disease


Computed tomography

FEV1 :

Forced expiratory volume in 1 second


Global Initiative for Chronic Obstructive Lung Disease


Hazard ratio


Hounsfield units


Korean Obstructive Lung Disease


Least absolute shrinkage and selection operation


Radiomics signature


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Correspondence to Joon Beom Seo.

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The scientific guarantor of this publication is Joon Beom Seo.

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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, Jeong Eun Hwang, has significant statistical expertise.

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Written informed consent was obtained from all subjects in this study.

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Institutional Review Board approval was obtained.


• retrospective

• retrospective cohort study

• multicenter study

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Cho, Y.H., Seo, J.B., Lee, S.M. et al. Radiomics approach for survival prediction in chronic obstructive pulmonary disease. Eur Radiol 31, 7316–7324 (2021).

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  • Pulmonary disease, chronic obstructive
  • Computer-assisted radiographic image interpretation
  • Tomography, X-ray computed
  • Survival analysis
  • Thorax