Radiomics approach for survival prediction in chronic obstructive pulmonary disease

Abstract

Objectives

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

Methods

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.

Results

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.

Conclusions

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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Abbreviations

ADO:

Age, dyspnea, obstruction

ANOLD:

Asian Network of Obstructive Lung Disease

BMI:

Body mass index

BODE:

Body mass index, obstruction, dyspnea, exercise

C-index:

Harrell’s concordance index

COPD:

Chronic obstructive pulmonary disease

CT:

Computed tomography

FEV1 :

Forced expiratory volume in 1 second

GOLD:

Global Initiative for Chronic Obstructive Lung Disease

HR:

Hazard ratio

HU:

Hounsfield units

KOLD:

Korean Obstructive Lung Disease

LASSO:

Least absolute shrinkage and selection operation

RS:

Radiomics signature

References

  1. 1.

    Barnes PJ, Burney PG, Silverman EK et al (2015) Chronic obstructive pulmonary disease. Nat Rev Dis Primers 1:15076

    Article  Google Scholar 

  2. 2.

    Agusti A (2014) The path to personalised medicine in COPD. Thorax 69:857–864

    Article  Google Scholar 

  3. 3.

    Coxson HO, Leipsic J, Parraga G, Sin DD (2014) Using pulmonary imaging to move chronic obstructive pulmonary disease beyond FEV1. Am J Respir Crit Care Med 190:135–144

    CAS  Article  Google Scholar 

  4. 4.

    Vogelmeier CF, Criner GJ, Martinez FJ et al (2017) Global strategy for the diagnosis, management and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Respirology 22:575–601

    Article  Google Scholar 

  5. 5.

    Cho YH, Seo JB, Kim N et al (2015) Comparison of a new integral-based half-band method for CT measurement of peripheral airways in COPD with a conventional full-width half-maximum method using both phantom and clinical CT images. J Comput Assist Tomogr 39:428–436

    PubMed  Google Scholar 

  6. 6.

    Kirby M, van Beek EJR, Seo JB et al (2017) Management of COPD: is there a role for quantitative imaging? Eur J Radiol 86:335–342

    Article  Google Scholar 

  7. 7.

    Lynch DA, Austin JH, Hogg JC et al (2015) CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society. Radiology 277:192–205

    Article  Google Scholar 

  8. 8.

    Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957

    Article  Google Scholar 

  9. 9.

    Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  10. 10.

    Lao J, Chen Y, Li ZC et al (2017) A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep 7:10353

    Article  Google Scholar 

  11. 11.

    Segal E, Sirlin CB, Ooi C et al (2007) Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25:675–680

    CAS  Article  Google Scholar 

  12. 12.

    Park TS, Lee JS, Seo JB et al (2014) Study design and outcomes of Korean Obstructive Lung Disease (KOLD) cohort study. Tuberc Respir Dis (Seoul) 76:169–174

    Article  Google Scholar 

  13. 13.

    Loh LC, Oh YM, Lee SD, ANOLD Researchers (2015) The Asian Network for Obstructive Lung Disease (ANOLD)-COPD from an Asian perspective. QJM 108:921–922

  14. 14.

    Bae J, Kim N, Lee SM, Seo JB, Kim HC (2014) Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT. Med Phys 41:041908

    Article  Google Scholar 

  15. 15.

    Cho YH, Lee SM, Seo JB et al (2018) Quantitative assessment of pulmonary vascular alterations in chronic obstructive lung disease: associations with pulmonary function test and survival in the KOLD cohort. Eur J Radiol 108:276–282

    Article  Google Scholar 

  16. 16.

    Hwang J, Lee M, Lee SM et al (2016) A size-based emphysema severity index: robust to the breath-hold-level variations and correlated with clinical parameters. Int J Chron Obstruct Pulmon Dis 11:1835–1841

    Article  Google Scholar 

  17. 17.

    Kim EY, Seo JB, Lee HJ et al (2015) Detailed analysis of the density change on chest CT of COPD using non-rigid registration of inspiration/expiration CT scans. Eur Radiol 25:541–549

    Article  Google Scholar 

  18. 18.

    Kim N, Seo JB, Song KS, Chae EJ, Kang SH (2008) Semi-automatic measurement of the airway dimension by computed tomography using the full-with-half-maximum method: a study of the measurement accuracy according to the orientation of an artificial airway. Korean J Radiol 9:236–242

    Article  Google Scholar 

  19. 19.

    Lee E, Seo JB, Lee HJ et al (2015) Quantitative assessment of global and regional air trappings using non-rigid registration and regional specific volume change of inspiratory/expiratory CT scans: studies on healthy volunteers and asthmatics. Korean J Radiol 16:632–640

    Article  Google Scholar 

  20. 20.

    Lee SM, Seo JB, Lee SM, Kim N, Oh SY, Oh YM (2016) Optimal threshold of subtraction method for quantification of air-trapping on coregistered CT in COPD patients. Eur Radiol 26:2184–2192

    Article  Google Scholar 

  21. 21.

    Lee YJ, Lee M, Kim N, Seo JB, Park JY (2014) Automatic left and right lung separation using free-formed surface fitting on volumetric CT. J Digit Imaging 27:538–547

    Article  Google Scholar 

  22. 22.

    Oh SY, Lee M, Seo JB et al (2017) Size variation and collapse of emphysema holes at inspiration and expiration CT scan: evaluation with modified length scale method and image co-registration. Int J Chron Obstruct Pulmon Dis 12:2043–2057

    Article  Google Scholar 

  23. 23.

    Park S, Lee SM, Kim N, Seo JB, Shin H (2013) Automatic reconstruction of the arterial and venous trees on volumetric chest CT. Med Phys 40:071906

    Article  Google Scholar 

  24. 24.

    Cho YH, Seo JB, Lee SM et al (2018) Quantitative CT imaging in chronic obstructive pulmonary disease: review of current status and future challenges. J Korean Soc Radiol 78:1–12

    Article  Google Scholar 

  25. 25.

    Estepar RS, Kinney GL, Black-Shinn JL et al (2013) Computed tomographic measures of pulmonary vascular morphology in smokers and their clinical implications. Am J Respir Crit Care Med 188:231–239

    Article  Google Scholar 

  26. 26.

    Hackx M, Bankier AA, Gevenois PA (2012) Chronic obstructive pulmonary disease: CT quantification of airways disease. Radiology 265:34–48

    Article  Google Scholar 

  27. 27.

    Labaki WW, Martinez CH, Martinez FJ et al (2017) The role of chest computed tomography in the evaluation and management of the patient with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 196:1372–1379

    Article  Google Scholar 

  28. 28.

    Lee YK, Oh YM, Lee JH et al (2008) Quantitative assessment of emphysema, air trapping, and airway thickening on computed tomography. Lung 186:157–165

    Article  Google Scholar 

  29. 29.

    Matsuoka S, Kurihara Y, Yagihashi K, Hoshino M, Watanabe N, Nakajima Y (2008) Quantitative assessment of air trapping in chronic obstructive pulmonary disease using inspiratory and expiratory volumetric MDCT. AJR Am J Roentgenol 190:762–769

    Article  Google Scholar 

  30. 30.

    Matsuoka S, Yamashiro T, Washko GR, Kurihara Y, Nakajima Y, Hatabu H (2010) Quantitative CT assessment of chronic obstructive pulmonary disease. Radiographics 30:55–66

    Article  Google Scholar 

  31. 31.

    Camp RL, Dolled-Filhart M, Rimm DL (2004) X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 10:7252–7259

    CAS  Article  Google Scholar 

  32. 32.

    Heussel CP, Herth FJ, Kappes J et al (2009) Fully automatic quantitative assessment of emphysema in computed tomography: comparison with pulmonary function testing and normal values. Eur Radiol 19:2391–2402

    CAS  Article  Google Scholar 

  33. 33.

    Han MK, Kazerooni EA, Lynch DA et al (2011) Chronic obstructive pulmonary disease exacerbations in the COPDGene study: associated radiologic phenotypes. Radiology 261:274–282

    Article  Google Scholar 

  34. 34.

    Haruna A, Muro S, Nakano Y et al (2010) CT scan findings of emphysema predict mortality in COPD. Chest 138:635–640

    Article  Google Scholar 

  35. 35.

    Jairam PM, van der Graaf Y, Lammers JW, Mali WP, de Jong PA, group PS (2015) Incidental findings on chest CT imaging are associated with increased COPD exacerbations and mortality. Thorax 70:725–731

    Article  Google Scholar 

  36. 36.

    Muller NL, Staples CA, Miller RR, Abboud RT (1988) “Density mask”. An objective method to quantitate emphysema using computed tomography. Chest 94:782–787

    CAS  Article  Google Scholar 

  37. 37.

    Marin JM, Alfageme I, Almagro P et al (2013) Multicomponent indices to predict survival in COPD: the COCOMICS study. Eur Respir J 42:323–332

    Article  Google Scholar 

  38. 38.

    Steyerberg EW, Moons KG, van der Windt DA et al (2013) Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 10:e1001381

    Article  Google Scholar 

  39. 39.

    Belchi F, Pirashvili M, Conway J, Bennett M, Djukanovic R, Brodzki J (2018) Lung topology characteristics in patients with chronic obstructive pulmonary disease. Sci Rep 8:5341

    Article  Google Scholar 

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Funding

The authors state that this work has not received any funding.

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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.

Informed consent

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

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Institutional Review Board approval was obtained.

Methodology

• 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 (2021). https://doi.org/10.1007/s00330-021-07747-7

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Keywords

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