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Computed tomography on lung cancer screening is useful for adjuvant comorbidity diagnosis in developing countries

Juliane Nascimento de Mattos, Carlos Eugênio Santiago Escovar, Manuela Zereu, Adalberto Sperb Rubin, Spencer Marcantonio Camargo, Tan-Lucien Mohammed, Ricardo Sales dos Santos, Nupur Verma, Diana Penha Pereira, Erique Guedes Pinto, Tiago Machuca, Tássia Machado Medeiros, Bruno Hochhegger
ERJ Open Research 2022 8: 00061-2022; DOI: 10.1183/23120541.00061-2022
Juliane Nascimento de Mattos
1Graduate Program in Pathology, Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre, Brazil
2Medical Imaging Research Lab, LABIMED, Dept of Radiology, Pavilhão Pereira Filho Hospital, Irmandade Santa Casa de Misericórdia de Porto Alegre, Porto Alegre, Brazil
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  • ORCID record for Juliane Nascimento de Mattos
  • For correspondence: julianenmattos@gmail.com
Carlos Eugênio Santiago Escovar
3Santa Casa de Misericórdia de Porto Alegre, Porto Alegre, Brazil
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Manuela Zereu
3Santa Casa de Misericórdia de Porto Alegre, Porto Alegre, Brazil
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Adalberto Sperb Rubin
3Santa Casa de Misericórdia de Porto Alegre, Porto Alegre, Brazil
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Spencer Marcantonio Camargo
3Santa Casa de Misericórdia de Porto Alegre, Porto Alegre, Brazil
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Tan-Lucien Mohammed
4Dept of Radiology, College of Medicine, University of Florida, Gainesville, FL, USA
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Ricardo Sales dos Santos
5Dept of Radiology, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
6Israelita Albert Einstein Hospital, São Paulo, Brazil
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Nupur Verma
4Dept of Radiology, College of Medicine, University of Florida, Gainesville, FL, USA
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Diana Penha Pereira
7Liverpool Heart and Chest Hospital, Liverpool, UK
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Erique Guedes Pinto
8Dept of Radiology, Lincoln County Hospital, United Lincolnshire Hospitals NHS Trust, Lincoln, UK
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Tiago Machuca
4Dept of Radiology, College of Medicine, University of Florida, Gainesville, FL, USA
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Tássia Machado Medeiros
9Postgraduate Program in Medicine and Health Sciences, Pontificia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
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Bruno Hochhegger
1Graduate Program in Pathology, Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre, Brazil
2Medical Imaging Research Lab, LABIMED, Dept of Radiology, Pavilhão Pereira Filho Hospital, Irmandade Santa Casa de Misericórdia de Porto Alegre, Porto Alegre, Brazil
4Dept of Radiology, College of Medicine, University of Florida, Gainesville, FL, USA
10Dept of Radiology, Federal University of Health Sciences of Porto Alegre, Porto Alegre, Brazil
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Abstract

Purpose The aim of this study was to analyse and quantify the prevalence of six comorbidities from lung cancer screening (LCS) on computed tomography (CT) scans of patients from developing countries.

Methods For this retrospective study, low-dose CT scans (n=775) were examined from patients who underwent LCS in a tertiary hospital between 2016 and 2020. An age- and sex-matched control group was obtained for comparison (n=370). Using the software, coronary artery calcification (CAC), the skeletal muscle area, interstitial lung abnormalities, emphysema, osteoporosis and hepatic steatosis were accessed. Clinical characteristics of each participant were identified. A t-test and Chi-squared test were used to examine differences between these values. Interclass correlation coefficients (ICCs) and interobserver agreement (assessed by calculating kappa coefficients) were calculated to assess the correlation of measures interpreted by two observers. p-values <0.05 were considered significant.

Results One or more comorbidities were identified in 86.6% of the patients and in 40% of the controls. The most prevalent comorbidity was osteoporosis, present in 44.2% of patients and in 24.8% of controls. New diagnoses of cardiovascular disease, emphysema and osteoporosis were made in 25%, 7% and 46% of cases, respectively. The kappa coefficient for CAC was 0.906 (p<0.001). ICCs for measures of liver, spleen and bone density were 0.88, 0.93 and 0.96, respectively (p<0.001).

Conclusions CT data acquired during LCS led to the identification of previously undiagnosed comorbidities. The LCS is useful to facilitate comorbidity diagnosis in developing countries, providing opportunities for its prevention and treatment.

Abstract

Lung cancer screening is useful to facilitate comorbidity diagnosis in developing countries, providing opportunities for its prevention and treatment https://bit.ly/3KEdGuW

Introduction

Lung cancer is the second most prevalent cancer and the leading cause of cancer death in 2020, with 2.2 million new cancer cases and 1.8 million deaths [1]. It has been one of the main causes of cancer deaths worldwide for >30 years, with a 5-year survival rate of 20.5% in the period 2010–2016 and 21.7% in the period 2011–2017 [2, 3]. This low survival rate is due in part to late diagnosis (only 16% of cases are diagnosed in the initial stage) and the complexity of its symptoms; it can appear late with smoking-related comorbidities [3, 4].

Lung cancer incidence and mortality rates are higher in developed countries than in developing countries, and this pattern may well change as the tobacco epidemic evolves owing to most smokers being from developing countries [5]. In Brazil, the number of lung cancer cases is increasing continuously due to increasing smoking rates, and the government has implemented a campaign for tobacco control to reduce the prevalence of smokers [6].

The National Lung Screening Trial (NLST) conducted in 2002–2009 demonstrated that low-dose computed tomography (CT) could reduce lung cancer mortality in current and ex-smokers by identifying suspected cases of lung cancer at an early stage [4]. Screening with low-dose CT reduced mortality from lung cancer by 20.0% compared with chest radiograph-based screening [4]. Nevertheless, the problematic nature of smoking-related comorbidities has been described in reports on lung cancer screening (LCS) [4]. Cigarette smoking affects multiple organ systems, and most trial participants are unaware of the comorbidities they have, which can result in death during the study period [4].

Chronic diseases related to smoking identified in previous LCS studies, and commonly found on chest CT, include osteoporosis and pulmonary (emphysema and interstitial lung abnormalities (ILAs)) and cardiovascular disease [7–9]. ILAs are non-dependent abnormalities identified incidentally in patients without clinical suspicion of interstitial lung disease (ILD), when ILD may be compatible with these abnormalities [10, 11]. Furthermore, there are subcategories of ILAs including: non-subpleural, subpleural non-fibrotic and subpleural fibrotic [9, 11].

Other smoking-related diseases also identified in LCS studies and associated with lung cancer such as hepatic steatosis and sarcopenia were also considered in this study [12, 13]. Sarcopenia is characterised by the loss of muscle mass and function and affects mainly elderly adults, such as those participating in our study [14–16]. The identification of these conditions in LCS participants is important, as it enables their treatment and contributes to patient prognoses [4, 7, 17].

Chronic diseases are increasing in global prevalence and seriously threaten developing nations’ ability to improve the health of their populations. Although often associated with developed nations, the presence of chronic disease has become the dominant health burden in many developing countries. The rise of lifestyle-related chronic disease in poor countries is the result of a complex constellation of social, economic and behavioural factors [18].

This study aimed to analyse and quantify the prevalence of six smoking-related comorbidities (osteoporosis, sarcopenia, emphysema, ILAs, coronary artery calcification (CAC) and hepatic steatosis) in a cohort of patients undergoing LCS via thoracic CT assessment in developing countries.

Patients and methods

For this retrospective study, low-dose CT scans from patients who underwent LCS in a tertiary hospital between 2016 and 2020 were examined. Subjects aged >55 and <80 years with a smoking history of at least 30 pack-years (packs per day×years smoked) or who had quit smoking within the previous 15 years were included. Some patients were excluded owing to CT motion artefacts (19) and others owing to the unavailability of images (10); the final cohort consisted of 775 patients (602 men) with a mean±SD age of 64±6.8 years. All patients included were smokers or ex-smokers (median 36.7 pack-years (range 30–79)). Patients’ medical records were reviewed to identify any previous diagnoses of the comorbidities examined in this study. Control patients (n=370; 288 men with a mean age of 62±6.5 years) matched by age and sex having a low-dose CT scan for pulmonary nodule controls were reviewed for comparison. These patients were selected randomly from the same time period as the screening lung cancer group. The control participants were from a cohort of a programme in a tertiary hospital staging extrathoracic skin cancer. All controls included were also smokers or ex-smokers (median 23.5 pack-years (range 2–70)). Reviewers were blinded because of the nature of the respective scans (CT). This study was approved by the institution's research ethics committee (58815316.9.0000.5335). Informed consent was not required. CT settings are described in supplementary table S1.

Image interpretation

A protocol (table 1) was developed for the quantitative evaluation of the CT images, which included assessment for CAC, ILAs and emphysema, determination of the skeletal muscle area (SMA) and examination of vertebral bone density, and liver fat analysis using Chest Imaging Platform software (Applied Chest Imaging Laboratory/Brigham and Women's Hospital, Boston, MA, USA). Two radiologists with >10 years of chest radiology experience and training in thoracic anatomy and the features of the software used performed the CAC, ILAs, emphysema, vertebral bone density and liver fat analyses.

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TABLE 1

The computed tomography (CT) protocol evaluation parameters for the six comorbidities described

CT assessment of comorbidities

CAC

CAC evaluation was performed using the visual calcium method, based on previous studies [17, 19]. The radiologists ranked the presence of calcium in the coronary artery (mild, moderate and severe) on the CT images. They also performed segmented vessel-specific scoring using an ordinal scale of 0–3: 0=no CAC; 1=mild CAC (<1-cm calcification plaque); 2=moderate CAC (1–2-cm calcification plaque); 3=heavy CAC (>2-cm calcification plaque) (figure 1).

FIGURE 1
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FIGURE 1

Examples of coronary artery calcification (CAC) analysis for the diagnosis of coronary artery disease from axial computed tomography images acquired without contrast. a) Mild CAC (score=1) in a woman aged 65 years. b) Moderate CAC (score=2) in a woman aged 55 years. c) Heavy CAC (score=3) in a woman aged 60 years.

Vertebral bone density

To evaluate osteoporosis, quantitative bone density analysis was performed using the previously described software (3DSlicer) [20]. Using a region of interest (ROI) tool (areas of 1.5–3 cm2), bone density in the T12 vertebral region was measured on the CT images in Hounsfield units (HU) while avoiding cortical bone (figure 2). Densities <100 HU were considered to indicate osteoporosis, according to a previous study [20].

FIGURE 2
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FIGURE 2

A 75-year-old woman with osteoporosis. a) Bone density was evaluated in the T12 region. b) A region of interest (area, 1.5–3 cm2) was placed while avoiding cortical bone for the determination of bone density in Hounsfield units.

SMA

Sarcopenia was evaluated in the lumbar region (L3) using the reference values of the European Working Group on Sarcopenia in Older People [15] in the previously described software (3DSlicer). The SMA and the mean radiation attenuation of skeletal muscle were determined using previously described methods [16, 21, 22]. The HU range used for skeletal muscle was −29 to 150 HU. The L3 muscle index (centimetres squared/metres squared) was defined as the cross-sectional area of muscle at the L3 level, normalised for stature as is conventional for body mass index calculation. L3 muscle indices <55 cm2/m2 for men and <39 cm2/m2 for women were considered to indicate sarcopenia (figure 3) [23].

FIGURE 3
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FIGURE 3

A 60-year-old man with sarcopenia. Computed tomography images extending inferiorly from L3 were evaluated automatically. After application of a predefined Hounsfield unit threshold, the boundaries were corrected manually when necessary.

Pulmonary densitometry

Emphysema and ILAs were evaluated based on the quantification of pulmonary density and volume (figure 4) using the described software (3DSlicer), with linear attenuation values ranging from –1000 to 3095 HU [24]. The emphysema index (EI) was calculated as the percentage of low attenuation areas (LAA <–950 HU) in the lung parenchyma [24–26]. EI values >2.5% were taken to reflect emphysema [26]. High attenuation areas (HAAs; –600 to −250 HU) were also identified, and ILAs were defined as HAA% >9.77% [27]. The total pulmonary volume (in millilitres) was also calculated.

FIGURE 4
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FIGURE 4

Examples of pulmonary densitometry analysis for the diagnosis of interstitial lung abnormalities (ILAs) and emphysema using reconstruction and automated detection. a) Severe emphysema in a 65-year-old man. b) Moderate ILAs in a 65-year-old man.

Liver fat analysis

The diagnosis of hepatic steatosis was based on the analysis of liver fat (figure 5). Liver and spleen average attenuation was assessed using the ROI tools of the described software (3DSlicer) [12, 28]. ROIs of the same size (areas of 1.5–3 cm2) were placed in the left lateral, left medial, right anterior and right posterior hepatic sections and in the upper, middle and lower thirds of the spleen with the avoidance of large vessels and lesions. The difference between the mean attenuations of the two organs was then calculated for the establishment of a cutoff point for the diagnosis of hepatic steatosis (difference in attenuation >10 HU).

FIGURE 5
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FIGURE 5

A 65-year-old man with hepatic steatosis. Attenuation of the liver (regions 2 and 4) and spleen (region 3) was assessed using region of interest tools in post-processing programs.

Statistical analysis

The statistical analyses were performed using SPSS software (ver. 2.6; SPSS Inc., Chicago, IL, USA). Continuous variables distributed normally were expressed as mean±SD. Discrete variables were expressed as frequencies with percentages. Clinical characteristics of each participant were identified with the mean of lung densitometry values, and the t-test and Chi-squared test were used to examine differences between these values. Interobserver agreement was assessed by calculating kappa coefficients. Interclass correlation coefficients (ICCs) were calculated to assess the correlation of the reliability measures interpreted by the two observers, such as measures of liver, spleen and bone density. p-values <0.05 were considered significant.

Results

A total of 775 thoracic CT scans were analysed for the presence of CAC, emphysema, ILAs, sarcopenia, osteoporosis and hepatic steatosis. The mean or median lung densitometry values found in CT scans are given in table 2. One or more comorbidities were identified in 86.6% of the patients and in 40% of controls (table 3). Emphysema was identified in 66.3% of patients and in 8.9% of controls, osteoporosis was present in 44.2% of patients and in 24.8% of controls, CAC was identified in 41.9% of cases and in 23.7% of controls, hepatic steatosis was identified in 40.7% of cases and in 15.9% of controls, ILAs were identified in 32.2% of patients and in 12.7% of controls, and sarcopenia was identified in 9.9% of patients and in 3.5% of controls (table 3). New diagnoses of cardiovascular disease (comorbidities not previously diagnosed according to the medical records) were made for 25% of patients, emphysema was newly diagnosed in 7% of patients and osteoporosis was newly diagnosed in 46% of patients.

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TABLE 2

Low-dose computed tomography (CT) findings in patients undergoing lung cancer screening (n=775)

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TABLE 3

Comorbidities identified in patients undergoing lung cancer screening low-dose computed tomography (LDCT) (n=775) and control group (n=370)

The kappa coefficient for CAC was 0.906 (p<0.001). ICCs for measures of liver, spleen and bone density were 0.88, 0.93 and 0.96, respectively (all p<0.001).

Discussion

A complex constellation of social, economic and behavioural factors is behind the rise in chronic diseases. With the luxury of hindsight, we can apply some of the lessons learned in developed countries to developing countries, but only to a limited extent. The three main risk factors for chronic diseases - overnutrition, lack of physical activity and tobacco use - are increasing generally in developing countries, just as in developed countries [18]. Poor healthcare is an important risk factor for the development of chronic diseases. As a general rule, in poor developing countries there are problems with access to healthcare and affordability of preventive care [18]. Also, primary care systems are weak and often too overloaded to respond to emerging chronic disease symptoms. Because of this, any improvement in diagnosis such as that discussed in this study could be important to improvement in health in developing countries. In our study, one or more comorbidities beyond lung cancer were identified in 86.6% of patients who underwent elective low-dose CT examinations for LCS.

The most prevalent comorbidity was pulmonary emphysema, identified in the majority of study participants. Lung cancer and pulmonary emphysema have the common cause of cigarette smoking, and emphysema is a risk factor for the development of lung cancer [7, 29, 30]. Emphysema is a common, usually incidental, CT finding in patients undergoing LCS [30]. Our results thus corroborate previous findings. As emphysema is a risk factor for early mortality among individuals with asymptomatic COPD, its diagnosis during LCS is very important, as it enables measures to be taken to improve patient prognoses and reduce participant death during studies [30, 31]. ILAs were also identified among the participants, and previous studies demonstrated the strong association between ILAs and mortality among smokers and individuals with COPD [27, 32]. ILAs were also reported on CT scans from previous LCS studies [8, 9].

Osteoporosis and CAC had similar prevalence rates in this study, and together were identified on >80% of the CT scans evaluated. CT enables better quantification of bone density for the diagnosis of smoking-related diseases and more effective prediction of bone fractures than does dual-energy radiograph absorptiometry (DXA) [7, 33, 34]. In this study, low bone mineral density could be identified by low-dose CT during LCS, confirming previous findings [7, 20]. Such an examination is thus a viable option for the diagnosis of osteoporosis in individuals at high risk of fracture who meet the inclusion criteria used in this study, with no need for additional imaging. CAC, the third most prevalent smoking-related comorbidity identified in this study, is a risk factor for cardiovascular disease [17]. Other than lung cancer, cardiovascular disease was the main cause of death among NLST participants [4, 17]. Some researchers have suggested that CAC quantification increases the cost–benefit ratio of LCS, as it enables measures to be taken to reduce mortality among trial participants, although no consensus on this issue has been reached in the scientific community [29, 35, 36].

Hepatic steatosis was diagnosed on 40.7% of the CT scans evaluated in this study. Low-dose CT examination performed without contrast is an objective, reproducible and noninvasive means of measuring liver fat [12, 28]. Chen et al. (2017) [12] demonstrated that hepatic steatosis can be identified via the quantification of liver and spleen density on CT scans acquired during LCS. This measure adds value to studies and contributes to the quality of life of participants in whom this comorbidity is discovered and subsequently treated. The prevalence of fatty liver described in this study corroborates with previous studies about prevalence of hepatic steatosis in LCS [12].

Another prominent comorbidity identified in this study was sarcopenia. Sarcopenia has a high impact on public health because it is associated with many comorbidities, such as osteoporosis [37]. One cohort demonstrated sarcopenia as a high risk factor for lung cancer recurrence, and also the tumours of patients with sarcopenia have higher malignant potential [13]. Imaging evaluations such as CT are considered to be the gold standard for its diagnosis, and they reduce the need for another test, but other modalities, such as DXA, are often preferred owing to their lower costs [37]. The present study demonstrates that LCS can be used to identify sarcopenia. This disease is associated with a greater risk of hospitalisation and may be responsible for unfavourable outcomes (i.e., reduced quality of life and mortality) in patients with cancer who have undergone vascular surgery [38].

Our study has limitations. First, our patient controls have a diagnosis of cancer and have less exposure to smoking than our patients. Although we demonstrated CT scans can be used for osteoporosis screening without additional imaging and radiation exposure, the accuracy of this method depends on clinical and population screening objectives, and more cohorts are needed to evaluate the sensitivity and specificity of diagnostic performance measurements [20, 39]. Considering this is a retrospective study, we could not change the patient management in the present study, although we demonstrated the high LCS potential of identifying smoking-related comorbidities, using CT scans, to provide an opportunity for treatment for the participants and increase the chances of a favourable outcome. This finding is important for future studies in this area.

Conclusion

This study showed that smoking-related comorbidities (CAC, emphysema, ILAs, sarcopenia, osteoporosis and hepatic steatosis) are prevalent in patients undergoing low-dose CT examination for LCS in developing countries. These findings demonstrate the importance of integrated CT assessment as part of LCS, as the identification of such comorbidities provides the opportunity to address them, increasing the chances of favourable prognoses and outcomes for the participants.

Supplementary material

Supplementary Material

Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.

Supplementary material 00061-2022.SUPPLEMENT

Footnotes

  • Provenance: Submitted article, peer reviewed.

  • Conflict of interest: The authors declare no conflict of interest.

  • Received February 2, 2022.
  • Accepted April 26, 2022.
  • Copyright ©The authors 2022
http://creativecommons.org/licenses/by-nc/4.0/

This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions{at}ersnet.org

References

  1. ↵
    1. Sung H,
    2. Ferlay J,
    3. Siegel RL, et al.
    Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021; 71: 209–249. doi:10.3322/caac.21660
    OpenUrlCrossRefPubMed
  2. ↵
    1. Bach PB,
    2. Mirkin JN,
    3. Oliver TK, et al.
    Benefits and harms of CT screening for lung cancer: a systematic review. JAMA 2012; 307: 2418–2429. doi:10.1001/jama.2012.5521
    OpenUrlCrossRefPubMed
  3. ↵
    1. National Institutes of Health
    . Cancer of the Lung and Bronchus – Cancer Stat Facts. SEER. www.seer.cancer.gov/statfacts/html/lungb.html Date last accessed: 4 March 2021.
  4. ↵
    1. NLST
    . Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011; 365: 395–409. doi:10.1056/NEJMoa1102873
    OpenUrlCrossRefPubMed
  5. ↵
    World Health Organization. WHO Global Report on Trends in Prevalence of Tobacco Smoking 2000–2025. 3rd Edn. Geneva, World Health Organization, 2019.
  6. ↵
    1. de Sá VK,
    2. Coelho JC,
    3. Capelozzi VL, et al.
    Lung cancer in Brazil: epidemiology and treatment challenges. Lung Cancer (Auckl) 2016; 7: 141–148.
    OpenUrl
  7. ↵
    1. Regan EA,
    2. Lowe KE,
    3. Make BJ, et al.
    Identifying smoking-related disease on lung cancer screening CT scans: increasing the value. Chronic Obstr Pulm Dis 2019; 6: 233–245.
  8. ↵
    1. Brown S-AW,
    2. Padilla M,
    3. Mhango G, et al.
    Interstitial lung abnormalities and lung cancer risk in the national lung screening trial. Chest 2019; 156: 1195–1203. doi:10.1016/j.chest.2019.06.041
    OpenUrl
  9. ↵
    1. Hatabu H,
    2. Hunninghake GM,
    3. Richeldi L, et al.
    Interstitial lung abnormalities detected incidentally on CT: a Position Paper from the Fleischner Society. Lancet Respir Med 2020; 8: 726–737. doi:10.1016/S2213-2600(20)30168-5
    OpenUrlPubMed
  10. ↵
    1. Hata A,
    2. Schiebler ML,
    3. Lynch DA, et al.
    Interstitial lung abnormalities: state of the art. Radiology 2021; 301: 19–34. doi:10.1148/radiol.2021204367
    OpenUrl
  11. ↵
    1. Hino T,
    2. Lee KS,
    3. Yoo H, et al.
    Interstitial lung abnormality (ILA) and nonspecific interstitial pneumonia (NSIP). Eur J Radiol Open 2021; 8: 100336. doi:10.1016/j.ejro.2021.100336
    OpenUrl
  12. ↵
    1. Chen X,
    2. Li K,
    3. Yip R, et al.
    Hepatic steatosis in participants in a program of low-dose CT screening for lung cancer. Eur J Radiol 2017; 94: 174–179. doi:10.1016/j.ejrad.2017.06.024
    OpenUrl
  13. ↵
    1. Kawaguchi Y,
    2. Hanaoka J,
    3. Ohshio Y, et al.
    Sarcopenia increases the risk of post-operative recurrence in patients with non-small cell lung cancer. PLoS One 2021; 16: e0257594.
    OpenUrl
  14. ↵
    1. Baumgartner RN,
    2. Stauber PM,
    3. McHugh D, et al.
    Cross-sectional age differences in body composition in persons 60+ years of age. J Gerontol A Biol Sci Med Sci 1995; 50: M307–M316. doi:10.1093/gerona/50A.6.M307
    OpenUrlPubMed
  15. ↵
    1. Cruz-Jentoft AJ,
    2. Baeyens JP,
    3. Bauer JM, et al.
    Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010; 39: 412–423. doi:10.1093/ageing/afq034
    OpenUrlCrossRefPubMed
  16. ↵
    1. Derstine BA,
    2. Holcombe SA,
    3. Ross BE, et al.
    Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep 2018; 8: 11369. doi:10.1038/s41598-018-29825-5
    OpenUrlPubMed
  17. ↵
    1. Chiles C,
    2. Duan F,
    3. Gladish GW, et al.
    Association of coronary artery calcification and mortality in the national lung screening trial: a comparison of three scoring methods. Radiology 2015; 276: 82–90. doi:10.1148/radiol.15142062
    OpenUrlCrossRefPubMed
  18. ↵
    1. Nugent R
    . Chronic diseases in developing countries: health and economic burdens. Ann N Y Acad Sci 2008; 1136: 70–79. doi:10.1196/annals.1425.027
    OpenUrlCrossRefPubMed
  19. ↵
    1. Neves PO,
    2. Andrade J,
    3. Monção H
    . Coronary artery calcium score: current status. Radiol Bras 2017; 50: 182–189. doi:10.1590/0100-3984.2015.0235
    OpenUrlCrossRef
  20. ↵
    1. Marinova M,
    2. Edon B,
    3. Wolter K, et al.
    Use of routine thoracic and abdominal computed tomography scans for assessing bone mineral density and detecting osteoporosis. Curr Med Res Opin 2015; 31: 1871–1881. doi:10.1185/03007995.2015.1074892
    OpenUrl
  21. ↵
    1. Goodpaster BH,
    2. Kelley DE,
    3. Thaete FL, et al.
    Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol 2000; 89: 104–110. doi:10.1152/jappl.2000.89.1.104
    OpenUrlCrossRefPubMed
  22. ↵
    1. Aubrey J,
    2. Esfandiari N,
    3. Baracos VE, et al.
    Measurement of skeletal muscle radiation attenuation and basis of its biological variation. Acta Physiol (Oxf) 2014; 210: 489–497. doi:10.1111/apha.12224
    OpenUrlCrossRefPubMed
  23. ↵
    1. Kim EY,
    2. Lee HY,
    3. Kim KW, et al.
    Preoperative computed tomography-determined sarcopenia and postoperative outcome after surgery for non-small cell lung cancer. Scand J Surg 2018; 107: 244–251.
    OpenUrl
  24. ↵
    1. Zach J,
    2. Newell J,
    3. Schroeder J, et al.
    Quantitative CT of the lungs and airways in healthy non-smoking adults. Invest Radiol 2012; 47: 596–602. doi:10.1097/RLI.0b013e318262292e
    OpenUrlCrossRefPubMed
    1. Grydeland TB,
    2. Thorsen E,
    3. Dirksen A, et al.
    Quantitative CT measures of emphysema and airway wall thickness are related to D(L)CO. Respir Med 2011; 105: 343–351. doi:10.1016/j.rmed.2010.10.018
    OpenUrlCrossRefPubMed
  25. ↵
    1. Wang Z,
    2. Gu S,
    3. Leader JK, et al.
    Optimal threshold in CT quantification of emphysema. Eur Radiol 2013; 23: 975–984. doi:10.1007/s00330-012-2683-z
    OpenUrlCrossRefPubMed
  26. ↵
    1. Lederer DJ,
    2. Enright PL,
    3. Kawut SM, et al.
    Cigarette smoking is associated with subclinical parenchymal lung disease: the Multi-Ethnic Study of Atherosclerosis (MESA)-lung study. Am J Respir Crit Care Med 2009; 180: 407–414. doi:10.1164/rccm.200812-1966OC
    OpenUrlCrossRefPubMed
  27. ↵
    1. Davidson LE,
    2. Kuk JL,
    3. Church TS, et al.
    Protocol for measurement of liver fat by computed tomography. J Appl Physiol (1985) 2006; 100: 864–868. doi:10.1152/japplphysiol.00986.2005
    OpenUrlCrossRefPubMed
  28. ↵
    1. Kucharczyk MJ,
    2. Menezes RJ,
    3. McGregor A, et al.
    Assessing the impact of incidental findings in a lung cancer screening study by using low-dose computed tomography. Can Assoc Radiol J 2011; 62: 141–145. doi:10.1016/j.carj.2010.02.008
    OpenUrlPubMed
  29. ↵
    1. Zulueta JJ,
    2. Wisnivesky JP,
    3. Henschke CI, et al.
    Emphysema scores predict death from COPD and lung cancer. Chest 2012; 141: 1216–1223. doi:10.1378/chest.11-0101
    OpenUrlCrossRefPubMed
  30. ↵
    1. de Torres JP,
    2. Bastarrika G,
    3. Wisnivesky JP, et al.
    Assessing the relationship between lung cancer risk and emphysema detected on low-dose CT of the chest. Chest 2007; 132: 1932–1938. doi:10.1378/chest.07-1490
    OpenUrlCrossRefPubMed
  31. ↵
    1. Podolanczuk AJ,
    2. Oelsner EC,
    3. Barr RG, et al.
    High attenuation areas on chest computed tomography in community-dwelling adults: the MESA study. Eur Respir J 2016; 48: 1442–1452. doi:10.1183/13993003.00129-2016
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. Romme EA,
    2. Murchison JT,
    3. Phang KF, et al.
    Bone attenuation on routine chest CT correlates with bone mineral density on DXA in patients with COPD. J Bone Miner Res 2012; 27: 2338–2343. doi:10.1002/jbmr.1678
    OpenUrlCrossRefPubMed
  33. ↵
    1. Jaramillo JD,
    2. Wilson C,
    3. Stinson DJ, 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. doi:10.1513/AnnalsATS.201412-591OC
    OpenUrl
  34. ↵
    1. van de Wiel JCM,
    2. Wang Y,
    3. Xu DM, et al.
    Neglectable benefit of searching for incidental findings in the Dutch-Belgian lung cancer screening trial (NELSON) using low-dose multidetector CT. Eur Radiol 2007; 17: 1474–1482. doi:10.1007/s00330-006-0532-7
    OpenUrlCrossRefPubMed
  35. ↵
    1. Mets OM,
    2. de Jong PA,
    3. Prokop M
    . Computed tomographic screening for lung cancer: an opportunity to evaluate other diseases. JAMA 2012; 308: 1433–1434. doi:10.1001/jama.2012.12656
    OpenUrlCrossRefPubMed
  36. ↵
    1. Beaudart C,
    2. Rizzoli R,
    3. Bruyère O, et al.
    Sarcopenia: burden and challenges for public health. Arch Public Health 2014; 72: 45. doi:10.1186/2049-3258-72-45
    OpenUrlPubMed
  37. ↵
    1. Cawthon PM,
    2. Fox KM,
    3. Gandra SR, et al.
    Do muscle mass, muscle density, strength, and physical function similarly influence risk of hospitalization in older adults? J Am Geriatr Soc 2009; 57: 1411–1419. doi:10.1111/j.1532-5415.2009.02366.x
    OpenUrlCrossRefPubMed
  38. ↵
    1. Pickhardt PJ,
    2. Pooler BD,
    3. Lauder T, et al.
    Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 2013; 158: 588–595. doi:10.7326/0003-4819-158-8-201304160-00003
    OpenUrlCrossRefPubMed
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ERJ Open Research: 8 (2)
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Computed tomography on lung cancer screening is useful for adjuvant comorbidity diagnosis in developing countries
Juliane Nascimento de Mattos, Carlos Eugênio Santiago Escovar, Manuela Zereu, Adalberto Sperb Rubin, Spencer Marcantonio Camargo, Tan-Lucien Mohammed, Ricardo Sales dos Santos, Nupur Verma, Diana Penha Pereira, Erique Guedes Pinto, Tiago Machuca, Tássia Machado Medeiros, Bruno Hochhegger
ERJ Open Research Apr 2022, 8 (2) 00061-2022; DOI: 10.1183/23120541.00061-2022

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Computed tomography on lung cancer screening is useful for adjuvant comorbidity diagnosis in developing countries
Juliane Nascimento de Mattos, Carlos Eugênio Santiago Escovar, Manuela Zereu, Adalberto Sperb Rubin, Spencer Marcantonio Camargo, Tan-Lucien Mohammed, Ricardo Sales dos Santos, Nupur Verma, Diana Penha Pereira, Erique Guedes Pinto, Tiago Machuca, Tássia Machado Medeiros, Bruno Hochhegger
ERJ Open Research Apr 2022, 8 (2) 00061-2022; DOI: 10.1183/23120541.00061-2022
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