대한영상의학회지 (Journal of the Korean Society of Radiology)

  • 제84권5호
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  • Pages.1123-1133
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  • 2023
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  • 1738-2637(pISSN)
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  • 2288-2928(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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CT 기반 딥러닝을 이용한 만성 폐쇄성 폐질환의 체성분 정량화와 질병 중증도

CT-Derived Deep Learning-Based Quantification of Body Composition Associated with Disease Severity in Chronic Obstructive Pulmonary Disease

  • 송재은 (강원대학교병원 영상의학과) ;
  • 박소현 (울산대학교 의과대학 서울아산병원 영상의학과, 영상의학연구소) ;
  • 임명남 (강원대학교병원 의생명연구원) ;
  • 이은주 (강원대학교병원 환경보건센터) ;
  • 차윤기 (성균관대학교 의과대학 삼성서울병원 영상의학과) ;
  • 윤현정 (중앙보훈병원 영상의학과) ;
  • 김우진 (강원대학교 의과대학 내과학교실)
  • Jae Eun Song (Department of Radiology, Kangwon National University Hospital) ;
  • So Hyeon Bak (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Myoung-Nam Lim (Department of Biomedical Research Institute, Kangwon National University Hospital) ;
  • Eun Ju Lee (Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital) ;
  • Yoon Ki Cha (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Hyun Jung Yoon (Department of Radiology, Veterans Health Service Medical Center) ;
  • Woo Jin Kim (Department of Internal Medicine, School of Medicine, Kangwon National University)
  • 투고 : 2022.11.09
  • 심사 : 2023.05.16
  • 발행 : 2023.09.01
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초록

목적 만성폐쇄성폐질환의 CT에서 자동 정량 측정된 체성분과 폐기능 또는 정량적 변수들 사이의 연관성을 알아보고자 하였다. 대상과 방법 총 290명의 만성폐쇄성폐질환 환자를 대상으로 연구하였다. 흉부 CT에서 근육 및 피하지방 부피, T12 레벨에서 근육 및 피하지방 면적 및 골 감쇠를 딥러닝 기반 분할 알고리즘을 사용하여 획득하였다. Parametric response mapping-derived emphysema (이하 PRMemph), PRM-derived functional small airway disease (이하 PRMfSAD) 및 기도 벽 두께(airway wall thickness; 이하 AWT)-Pi10을 정량적으로 평가하였다. Pearson 상관 분석을 사용하여 체성분과 결과 간의 연관성을 평가하였다. 결과 근육과 피하지방의 부피와 면적은 PRMemph와 PRMfSAD와 음의 상관관계를 보였다(p < 0.05). T12에서의 골밀도는 PRMemph와 음의 상관관계를 보였다(r = -0.1828, p = 0.002). 피하지방의 부피와 면적과 T12에서의 골밀도는 AWT-Pi10과 양의 상관관계를 보였다(r = 0.1287, p = 0.030; r = 0.1668, p = 0.005; r = 0.1279, p = 0.031). 반면에 근육 부피는 AWT-Pi10과 음의 상관관계를 보였다(r = -0.1966, p = 0.001). 근육 부피는 폐기능과 의미 있는 연과성을 보였다(p < 0.001). 결론 흉부 CT에서 정량적으로 평가된 체성분은 만성폐쇄성폐질환의 표현형 또는 중증도와 연관성을 보인다.

Purpose Our study aimed to evaluate the association between automated quantified body composition on CT and pulmonary function or quantitative lung features in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods A total of 290 patients with COPD were enrolled in this study. The volume of muscle and subcutaneous fat, area of muscle and subcutaneous fat at T12, and bone attenuation at T12 were obtained from chest CT using a deep learning-based body segmentation algorithm. Parametric response mapping-derived emphysema (PRMemph), PRM-derived functional small airway disease (PRMfSAD), and airway wall thickness (AWT)-Pi10 were quantitatively assessed. The association between body composition and outcomes was evaluated using Pearson's correlation analysis. Results The volume and area of muscle and subcutaneous fat were negatively associated with PRMemph and PRMfSAD (p < 0.05). Bone density at T12 was negatively associated with PRMemph (r = -0.1828, p = 0.002). The volume and area of subcutaneous fat and bone density at T12 were positively correlated with AWT-Pi10 (r = 0.1287, p = 0.030; r = 0.1668, p = 0.005; r = 0.1279, p = 0.031). However, muscle volume was negatively correlated with the AWT-Pi10 (r = -0.1966, p = 0.001). Muscle volume was significantly associated with pulmonary function (p < 0.001). Conclusion Body composition, automatically assessed using chest CT, is associated with the phenotype and severity of COPD.

키워드

참고문헌

  1. Benz E, Trajanoska K, Lahousse L, Schoufour JD, Terzikhan N, De Roos E, et al. Sarcopenia in COPD: a systematic review and meta-analysis. Eur Respir Rev 2019;28:190049 
  2. Martinez-Luna N, Orea-Tejeda A, Gonzalez-Islas D, Flores-Cisneros L, Keirns-Davis C, Sanchez-Santillan R, et al. Association between body composition, sarcopenia and pulmonary function in chronic obstructive pulmonary disease. BMC Pulm Med 2022;22:106 
  3. Attaway AH, Welch N, Hatipoglu U, Zein JG, Dasarathy S. Muscle loss contributes to higher morbidity and mortality in COPD: an analysis of national trends. Respirology 2021;26:62-71 
  4. Jones SE, Maddocks M, Kon SS, Canavan JL, Nolan CM, Clark AL, et al. Sarcopenia in COPD: prevalence, clinical correlates and response to pulmonary rehabilitation. Thorax 2015;70:213-218 
  5. Schols AM, Broekhuizen R, Weling-Scheepers CA, Wouters EF. Body composition and mortality in chronic obstructive pulmonary disease. Am J Clin Nutr 2005;82:53-59 
  6. Swallow EB, Reyes D, Hopkinson NS, Man WD, Porcher R, Cetti EJ, et al. Quadriceps strength predicts mortality in patients with moderate to severe chronic obstructive pulmonary disease. Thorax 2007;62:115-120 
  7. Dolliver WR, Diaz AA. Advances in chronic obstructive pulmonary disease imaging. Barc Respir Netw Rev 2020;6:128-143 
  8. Chen YW, Ramsook AH, Coxson HO, Bon J, Reid WD. Prevalence and risk factors for osteoporosis in individuals with COPD: a systematic review and meta-analysis. Chest 2019;156:1092-1110 
  9. Inoue D, Watanabe R, Okazaki R. COPD and osteoporosis: links, risks, and treatment challenges. Int J Chron Obstruct Pulmon Dis 2016;11:637-648 
  10. Lee K, Shin Y, Huh J, Sung YS, Lee IS, Yoon KH, et al. Recent issues on body composition imaging for sarcopenia evaluation. Korean J Radiol 2019;20:205-217 
  11. Pishgar F, Shabani M, Quinaglia A C Silva T, Bluemke DA, Budoff M, Barr RG, et al. Quantitative analysis of adipose depots by using chest CT and associations with all-cause mortality in chronic obstructive pulmonary disease: longitudinal analysis from MESArthritis ancillary study. Radiology 2021;299:703-711 
  12. Tanabe N, Sato S, Tanimura K, Oguma T, Sato A, Muro S, et al. Associations of CT evaluations of antigravity muscles, emphysema and airway disease with longitudinal outcomes in patients with COPD. Thorax 2021;76:295-297 
  13. Hwang HJ, Lee SM, Seo JB, Kim JE, Choi HY, Kim N, et al. Quantitative vertebral bone density seen on chest CT in chronic obstructive pulmonary disease patients: association with mortality in the Korean obstructive lung disease cohort. Korean J Radiol 2020;21:880-890 
  14. McDonald ML, Diaz AA, Ross JC, San Jose Estepar R, Zhou L, Regan EA, et al. Quantitative computed tomography measures of pectoralis muscle area and disease severity in chronic obstructive pulmonary disease. A cross-sectional study. Ann Am Thorac Soc 2014;11:326-334 
  15. McDonald MN, Diaz AA, Rutten E, Lutz SM, Harmouche R, San Jose Estepar R, et al. Chest computed tomography-derived low fat-free mass index and mortality in COPD. Eur Respir J 2017;50:1701134 
  16. Choi H, Park YS, Na KJ, Park S, Park IK, Kang CH, et al. Association of adipopenia at preoperative PET/CT with mortality in stage I non-small cell lung cancer. Radiology 2021;301:645-653 
  17. Joo I, Kwak MS, Park DH, Yoon SH. Fully automated waist circumference measurement on abdominal CT: comparison with manual measurements and potential value for identifying overweight and obesity as an adjunct output of CT scan. PLoS One 2021;16:e0254704 
  18. Kim JE, Park SJ, Kim YC, Min SI, Ha J, Kim YS, et al. Deep learning-based quantification of visceral fat volumes predicts posttransplant diabetes mellitus in kidney transplant recipients. Front Med (Lausanne) 2021;8:632097 
  19. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al. Standardisation of spirometry. Eur Respir J 2005;26:319-338 
  20. Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report. GOLD executive summary. Am J Respir Crit Care Med 2017;195:557-582 
  21. Bhatt SP, Washko GR, Hoffman EA, Newell JD Jr, Bodduluri S, Diaz AA, et al. Imaging advances in chronic obstructive pulmonary disease. Insights from the genetic epidemiology of chronic obstructive pulmonary disease (COPDGene) study. Am J Respir Crit Care Med 2019;199:286-301 
  22. Galban CJ, Han MK, Boes JL, Chughtai KA, Meyer CR, Johnson TD, et al. Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med 2012;18:1711-1715 
  23. Tolonen A, Pakarinen T, Sassi A, Kytta J, Cancino W, Rinta-Kiikka I, et al. Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: a review. Eur J Radiol 2021;145:109943 
  24. Bak SH, Kwon SO, Han SS, Kim WJ. Computed tomography-derived area and density of pectoralis muscle associated disease severity and longitudinal changes in chronic obstructive pulmonary disease: a case control study. Respir Res 2019;20:226 
  25. Tanimura K, Sato S, Fuseya Y, Hasegawa K, Uemasu K, Sato A, et al. Quantitative assessment of erector spinae muscles in patients with chronic obstructive pulmonary disease. novel chest computed tomography-derived index for prognosis. Ann Am Thorac Soc 2016;13:334-341 
  26. Spartalis M, Tzatzaki E, Moris D, Athanasiou A, Spartalis E. Morbidity, mortality, and obesity paradox. Ann Transl Med 2017;5:440 
  27. Wu TD, Ejike CO, Wise RA, McCormack MC, Brigham EP. Investigation of the obesity paradox in chronic obstructive pulmonary disease, according to smoking status, in the United States. Am J Epidemiol 2019;188:1977-1983 
  28. Pan J, Xu L, Lam TH, Jiang CQ, Zhang WS, Jin YL, et al. Association of adiposity with pulmonary function in older Chinese: Guangzhou Biobank Cohort Study. Respir Med 2017;132:102-108 
  29. Wehrmeister FC, Menezes AM, Muniz LC, Martinez-Mesa J, Domingues MR, Horta BL. Waist circumference and pulmonary function: a systematic review and meta-analysis. Syst Rev 2012;1:55 
  30. Higami Y, Ogawa E, Ryujin Y, Goto K, Seto R, Wada H, et al. Increased epicardial adipose tissue is associated with the airway dominant phenotype of chronic obstructive pulmonary disease. PLoS One 2016;11:e0148794 
  31. Diaz AA, Zhou L, Young TP, McDonald ML, Harmouche R, Ross JC, et al. Chest CT measures of muscle and adipose tissue in COPD: gender-based differences in content and in relationships with blood biomarkers. Acad Radiol 2014;21:1255-1261 
  32. Hamakawa Y, Tanabe N, Shima H, Terada K, Shiraishi Y, Maetani T, et al. Associations of pulmonary and extrapulmonary computed tomographic manifestations with impaired physical activity in symptomatic patients with chronic obstructive pulmonary disease. Sci Rep 2022;12:5608 
  33. Jaramillo JD, Wilson C, Stinson DS, Lynch DA, Bowler RP, Lutz S, et al. Reduced bone density and vertebral fractures in smokers. Men and COPD patients at increased risk. Ann Am Thorac Soc 2015;12:648-656 
  34. Bon J, Fuhrman CR, Weissfeld JL, Duncan SR, Branch RA, Chang CC, et al. Radiographic emphysema predicts low bone mineral density in a tobacco-exposed cohort. Am J Respir Crit Care Med 2011;183:885-890 
  35. Ohara T, Hirai T, Muro S, Haruna A, Terada K, Kinose D, et al. Relationship between pulmonary emphysema and osteoporosis assessed by CT in patients with COPD. Chest 2008;134:1244-1249 
  36. Langhammer A, Forsmo S, Lilleeng S, Johnsen R, Bjermer L. Effect of inhaled corticosteroids on forearm bone mineral density: the HUNT study, Norway. Respir Med 2007;101:1744-1752 
  37. Loke YK, Cavallazzi R, Singh S. Risk of fractures with inhaled corticosteroids in COPD: systematic review and meta-analysis of randomised controlled trials and observational studies. Thorax 2011;66:699-708