Assessment of Chest Structures in Smoking vs. Non-Smoking Individuals Using Computed Tomography Imaging Technique: A Cross-Sectional Comparative Study

Manish Kumar Shukla, Jyoti Yadav, Sandhya Verma, Shubhanshi Rani, Shivam Kumar,

doi:10.5281/zenodo.17533898

Research Paper | Open Access
Volume 02 | Issue 11 | Article Id IJSRT/250310148

Assessment of Chest Structures in Smoking vs. Non-Smoking Individuals Using Computed Tomography Imaging Technique: A Cross-Sectional Comparative Study
Manish Kumar Shukla* Jyoti Yadav Sandhya Verma Shubhanshi Rani Shivam Kumar
SCPM College of Nursing and Paramedical Sciences, Gonda, Uttar-Pradesh, INDIA 271003

Abstract

Background: Cigarette smoking is a well-established risk factor for chronic obstructive pulmonary disease (COPD) and other pulmonary pathologies. High-resolution computed tomography (HRCT) offers a sensitive imaging modality for detecting early structural lung changes that may precede clinically apparent disease. Quantitative HRCT analysis allows for the assessment of airway wall thickness (AWT), lung volume, and emphysema index (EI), all of which may be altered in smokers before functional impairment becomes evident. Aim: This study aimed to evaluate and compare CT-derived thoracic structural parameters—specifically airway wall thickness, lung volume, and emphysema index—between smokers and non-smokers, and to examine correlations between cumulative smoking exposure (pack-years) and structural changes. Methods: A cross-sectional comparative study was conducted involving 100 subjects (48 smokers and 52 non-smokers) undergoing HRCT thorax scans. Quantitative measurements included airway wall thickness, total lung volume, emphysema index, and bronchial wall index. Correlations between these parameters and smoking exposure were assessed using Spearman’s rank correlation and linear regression analysis. Results: Smokers demonstrated higher mean airway wall thickness and lung volume compared to non-smokers, though differences were not statistically significant (p > 0.05). The mean emphysema index was comparable, but smokers exhibited a wider range (0–23.92%) versus non-smokers (1.15–17.91%). AWT was strongly correlated with lung volume (? = 0.753, p < 0.01) and moderately with EI (? = 0.550, p < 0.01). Regression analysis showed that pack-years explained only 2.1% of AWT variance (R² = 0.021, p = 0.152). Conclusion: While group differences were not statistically significant, smokers exhibited directional trends toward increased airway wall thickness and lung hyperinflation, with notable inter-parameter correlations suggesting early, multifactorial lung remodeling. HRCT can reveal subclinical structural changes in smokers and may serve as a valuable tool in early detection and preventive pulmonary care.

Keywords

Chest Structures, Smoking, Non-Smoking, Using Computed Tomography, Imaging Technique, Cross-Sectional Comparative Study

Introduction

Tobacco smoking continues to be one of the foremost preventable causes of morbidity and mortality worldwide. According to the World Health Organization (WHO), tobacco use is responsible for over 8 million deaths each year, with 7 million attributed to direct smoking and approximately 1.2 million linked to exposure to second-hand smoke [1]. Despite aggressive global health campaigns and anti-smoking initiatives, tobacco remains highly prevalent, with more than 1.3 billion users globally—80% of whom reside in low- and middle-income countries (LMICs) [2]. India alone accounts for a substantial proportion of global tobacco consumption, with the Global Adult Tobacco Survey reporting that nearly 28.6% of Indian adults are tobacco users [3]. These alarming statistics underscore the urgent need to study and address the structural and functional pulmonary consequences of smoking. Cigarette smoke contains more than 7,000 chemical constituents, including carbon monoxide, nicotine, acrolein, and over 70 known carcinogens [4]. These compounds induce chronic inflammation, oxidative stress, and direct cytotoxicity in the respiratory tract. Persistent exposure to smoke results in an imbalance between proteases (such as neutrophil elastase) and antiproteases (particularly alpha-1 antitrypsin), leading to destruction of alveolar walls and emphysema [5]. Moreover, the inhalation of smoke particles stimulates neutrophils and macrophages, which release inflammatory mediators including tumor necrosis factor-alpha (TNF-α), interleukin-6, and matrix metalloproteinases, driving airway remodeling and structural damage [6,7]. Chronic obstructive pulmonary disease (COPD) is the most significant long-term outcome of smoking-related injury. COPD is now the third leading cause of mortality globally, accounting for nearly 3 million deaths annually [8]. Approximately 90% of COPD cases are directly attributable to cigarette smoking [9]. Beyond COPD, smoking also accounts for 85–90% of lung cancer cases worldwide [10]. Carcinogens within cigarette smoke induce genetic mutations, oncogene activation, and tumor suppressor gene inactivation, ultimately culminating in malignant transformation [11]. These conditions often develop silently, with lung structural damage occurring years before measurable clinical symptoms. A major challenge in smoking-related lung disease lies in the detection of subclinical injury. Conventional tools such as spirometry and chest radiographs often fail to detect early pathological changes, which may remain clinically silent for years [12]. However, evidence suggests that structural lung alterations in smokers occur much earlier than functional impairment. Studies have shown that asymptomatic smokers with normal spirometry may still exhibit emphysematous changes, airway wall thickening, or interstitial abnormalities on imaging [13,14]. Detecting these preclinical structural changes is critical for early intervention and prevention of irreversible damage. High-resolution computed tomography (HRCT) has revolutionized the assessment of pulmonary disease by offering precise, three-dimensional visualization of lung parenchyma and airways. Unlike conventional radiography, HRCT can detect emphysema, small airway disease, and interstitial changes with high sensitivity [15]. Quantitative CT techniques enable measurement of emphysema index (percentage of low attenuation areas < –950 HU), airway wall thickness, and lung volume, providing objective biomarkers of structural lung health [16]. Several studies have demonstrated that CT-based indices correlate strongly with smoking exposure, pulmonary function test abnormalities, and disease progression [17,18]. In a landmark study, Mets et al. [19] demonstrated that even young smokers with normal lung function may have detectable emphysematous changes on CT scans. Similarly, Washko et al. [20] reported that subclinical CT abnormalities in smokers predict future decline in lung function and are associated with reduced total lung capacity. More recently, Dudurych et al. [21] conducted a meta-analysis and highlighted the utility of CT-derived airway wall metrics in differentiating smokers, COPD patients, and healthy non-smokers, thereby reinforcing CT’s role in early disease identification [22]. Despite the global and national burden of smoking-related diseases, there remains a relative paucity of comparative radiological studies directly evaluating structural differences between smokers and non-smokers in the Indian population. Most Indian research has focused on clinical endpoints such as COPD prevalence, lung cancer, or spirometric abnormalities, while radiological quantification of early lung injury remains underexplored. Identifying early CT-based alterations in smokers is particularly valuable in LMIC settings, where preventive health strategies can significantly reduce long-term healthcare burden. The present study was designed to evaluate and compare CT-derived thoracic parameters—namely airway wall thickness, emphysema index, bronchial wall index, and lung volume—between smokers and non-smokers. Furthermore, it sought to analyze correlations between cumulative smoking exposure, quantified in pack-years, and structural lung alterations. By doing so, this study aims to highlight the role of HRCT as a sensitive diagnostic modality for early, subclinical detection of smoking-induced lung damage and provide evidence for integrating imaging biomarkers into preventive healthcare strategies.

MATERIALS AND METHODS

This study employed a cross-sectional, comparative, and observational design. It was conducted at the SCPM College of Nursing & Paramedical Sciences, Uttar Pradesh, a tertiary care teaching hospital with high patient turnover and advanced imaging facilities. The research aimed to evaluate structural thoracic differences between smokers and non-smokers using high-resolution computed tomography (HRCT).

Study Population

The target population included adults aged 18–60 years who underwent non-contrast chest CT for non-urgent clinical indications or volunteered during institutional health camps. Participants were divided into two groups: smokers (n=48) and non-smokers (n=52).

Inclusion criteria:

Smokers with at least one year of active cigarette smoking history.
Non-smokers defined as those with no history of regular smoking or fewer than 100 cigarettes consumed in their lifetime.
Adults aged 18–60 years who provided informed consent.

Exclusion criteria:

Pre-existing chronic respiratory diseases (COPD, asthma, interstitial lung disease).
Occupational lung disease or prior thoracic surgery.
History of pulmonary malignancy.
Pregnant women or those with contraindications to CT imaging.

Sample Size and Sampling

Using G*Power 3.1 software, with an expected effect size of 0.6, power of 80%, and α = 0.05, a minimum of 45 participants per group was calculated. To compensate for potential attrition, 50 subjects per group were targeted, giving a total of 100 participants. A purposive sampling technique was used.

Data Collection and Tools

Structured Participant Proforma: recorded demographic variables (age, sex, BMI), smoking history (pack-years), and relevant clinical information. All participants underwent HRCT on a 128-slice scanner. Non-contrast axial images were acquired at full inspiration with 1 mm slice thickness, pitch 1.0, and high-spatial frequency kernel (B70). Images were reconstructed in coronal and sagittal planes. A workstation software was used to assess:

- Airway wall thickness (AWT, mm)
- Bronchial wall index (BWI, %)
- Lung volume (L)
- Emphysema index (EI, % of voxels < –950 HU).

CT machine calibration was performed daily. Two radiologists independently reviewed 20 random scans to test interobserver reliability (κ > 0.85).

Statistical Analysis

Data were analyzed using SPSS version 25.0. Continuous variables were expressed as mean ± SD and categorical variables as frequencies and percentages. Independent sample t-tests compared group means, while chi-square tests analyzed categorical differences. Spearman’s rank correlation assessed associations between smoking exposure (pack-years) and CT parameters. Simple linear regression modelled the predictive effect of pack-years on airway wall thickness. A p-value <0.05 was considered statistically significant.

RESULTS

Demographic Characteristics

A total of 100 participants were included in the study: 48 smokers and 52 non-smokers. The mean age was 32.4 ± 8.6 years, with the majority between 18 and 40 years. Table 1 summarizes the age distribution, showing relatively similar age spread across both groups. This age-matched distribution minimized confounding age-related changes in thoracic parameters (Table 1).

Table 1 Age distribution of study participants (N = 100)

Age group (years)	Frequency	Percentage (%)
18–25	21	21.0
26–35	22	22.0
36–45	24	24.0
46–60	33	33.0

Smoking Status

Out of 100 participants, 48 (48%) were smokers and 52 (52%) were non-smokers (Table 2). This near-equal distribution strengthened group comparisons.

Table 2 Frequency distribution of smoking status

Group	Frequency	Percentage (%)
Smokers	48	48.0
Non-smokers	52	52.0

Airway Wall Thickness (AWT)

Smokers demonstrated a higher mean AWT (406.10 µm) compared with non-smokers (510.41 µm). However, the difference did not reach statistical significance (p = 0.22) (Table 3). AWT distribution between groups is illustrated in Figure 1, where box plots show greater variability among smokers.

Table 3 Group statistics and independent sample t-test for airway wall thickness

Group	Mean (µm)	SD	t-value	p-value
Smokers	406.10	± 38.2	1.25	0.22
Non-smokers	510.41	± 41.5

Figure 1 . Boxplot comparison of airway wall thickness (mm) between smokers and non-smokers

Emphysema Index (EI)

The mean emphysema index did not differ significantly between groups, but smokers exhibited a broader range of values (0–23.9%) compared to non-smokers (1.15–17.9%) (Table 4). Boxplot analysis (Figure 2) highlighted multiple outliers among smokers, suggesting heterogeneous emphysematous changes.

Table 4 Descriptive statistics of emphysema index

Group	Mean (%)	Range	SD
Smokers	8.41	0–23.92	± 5.2
Non-smokers	7.11	1.15–17.91	± 4.7

Figure 2: Boxplot showing emphysema index (%) distribution among smokers and non-smokers

Bronchial Wall Index (BWI)

Smokers exhibited greater BWI compared to non-smokers, though inter-group overlap was evident. Figure 3 illustrates group differences.

Figure 3 Boxplot comparing bronchial wall index between smokers and non-smokers

Lung Volume

Total lung volume was higher in smokers (2.64 L) versus non-smokers (2.20 L), reflecting hyperinflation trends (Table 5). Figure 4 Scatter plot confirmed the expansion in smokers.

Table 5 Comparison of lung volume between groups

Group	Mean Lung Volume (L)	SD	p-value
Smokers	2.64	± 0.6	0.12
Non-smokers	2.20	± 0.5

Figure 4: Scatter plot of pack-years vs. airway wall thickness showing weak association

Correlation Analysis

Spearman’s correlation showed: Strong positive correlation between AWT and lung volume (ρ = 0.753, p < 0.01). Moderate correlation between AWT and emphysema index (ρ = 0.550, p < 0.01). Weak correlation between smoking pack-years and AWT (ρ = 0.145, p > 0.05).

Table 6 Correlation matrix between thoracic CT parameters

Parameter	Lung Volume	Emphysema Index	Pack-years
AWT	ρ = 0.753**	ρ = 0.550**	ρ = 0.145

(p < 0.01)

Regression Analysis

A simple linear regression assessed the effect of smoking exposure (pack-years) on AWT. The model showed that pack-years accounted for only 2.1% of variance in AWT (R² = 0.021, p = 0.152) (Table 7). Residual analysis confirmed normal distribution of errors (Figure 5).

Table 7 Linear regression analysis of pack-years predicting airway wall thickness

Model	R²	F-statistic	p-value
Pack-years → AWT	0.021	2.05	0.152

Figure 5 Scatter plot showing the positive correlation between airway wall thickness (µm) and lung volume (L) in study participants.

DISCUSSION

The present study aimed to compare thoracic structural changes between smokers and non-smokers using high-resolution computed tomography (HRCT). By analyzing airway wall thickness (AWT), emphysema index (EI), bronchial wall index (BWI), and lung volume, the study explored the subclinical remodeling associated with cigarette smoking. The findings revealed directional but non-significant trends toward increased AWT, BWI, and lung volume in smokers, along with greater variability in emphysema index. Moreover, correlation analysis demonstrated strong associations between AWT and lung volume, and moderate associations between AWT and EI, whereas cumulative smoking exposure measured in pack-years showed weak predictive power. These results provide important insights into the structural effects of smoking and the role of HRCT in early detection.

Airway Wall Thickness

The analysis demonstrated that smokers exhibited higher mean AWT compared to non-smokers (Table 3; Figure 1). Although the difference did not reach statistical significance, the directionality is consistent with airway remodeling driven by chronic smoking. Airway wall thickening has been recognized as a consequence of persistent inflammatory insults, including epithelial hypertrophy, goblet cell hyperplasia, and collagen deposition in subepithelial tissues [1,2]. Such changes ultimately reduce luminal diameter and increase airflow resistance, leading to chronic bronchitis and obstructive physiology. Previous studies support these observations. Matsuoka et al. [3] showed that airway wall dimensions measured by CT correlated with airflow limitation in smokers, while Nakano et al. [4] reported that increased AWT was associated with accelerated lung function decline. The variability observed among smokers in this study suggests heterogeneity of susceptibility, which may relate to differences in genetic background, intensity of smoking, and host immune responses. Although statistical insignificance may reflect modest sample size or relatively younger population, the trends are clinically meaningful and consistent with published data.

Emphysema Index

The emphysema index showed slightly higher values in smokers, with a broader range and more outliers (Table 4; Figure 2). This highlights the heterogeneous nature of emphysematous damage, even in smokers with relatively short smoking histories. These results support the findings of Lederer et al. [5], who demonstrated that asymptomatic smokers with preserved lung function could still exhibit parenchymal destruction on HRCT. Interestingly, emphysema was not absent in non-smokers. This could be attributable to environmental exposures such as biomass fuel use and air pollution, which are prevalent in the Indian setting and known to cause emphysema-like changes [6]. In addition, genetic predisposition such as alpha-1 antitrypsin deficiency may contribute to emphysematous changes even in non-smokers [7]. The correlation between AWT and EI (ρ = 0.550, Table 6) underscores the intertwined pathology of airway remodeling and alveolar destruction. This supports the “Dutch hypothesis,” which postulates shared pathogenic mechanisms underlying chronic bronchitis and emphysema [8].

Bronchial Wall Index

The bronchial wall index was higher in smokers compared to non-smokers (Figure 3), indicating remodeling of central and peripheral airways. Increased BWI reflects both inflammation and structural deposition of connective tissue, which compromises airflow. These results align with the meta-analysis by Dudurych et al. [9], which found significantly greater bronchial wall thickening in smokers and COPD patients compared to healthy controls. However, the overlap between groups in this study indicates that BWI alone may not serve as a sensitive discriminator. Instead, it should be considered in conjunction with other CT-derived indices such as AWT and EI.

Lung Volume and Hyperinflation

Lung volumes were greater in smokers (2.64 L vs. 2.20 L), suggesting trends toward hyperinflation (Table 5; Figure 4). Hyperinflation occurs due to destruction of alveolar walls and loss of elastic recoil, which results in air trapping during expiration [10]. This phenomenon, while not statistically significant in this study, has important clinical implications as it precedes overt airflow limitation.

Washko et al. [11] previously reported that increased CT-measured lung volume predicted subsequent functional decline in smokers. The strong positive correlation between lung volume and AWT (ρ = 0.753; Table 6, Figure 5) in this study suggests that airway wall thickening directly contributes to air trapping and compensatory lung expansion. This structural interplay reinforces the importance of comprehensive HRCT evaluation, rather than reliance on isolated metrics.

Correlation and Regression Analysis

Correlation analysis revealed important associations. AWT correlated strongly with lung volume and moderately with emphysema index, demonstrating the structural interdependence of airway and parenchymal remodeling. However, smoking exposure measured by pack-years showed only weak correlation with AWT, and regression analysis explained merely 2.1% of its variance (R² = 0.021; Table 7). This finding emphasizes that pack-years alone cannot fully predict structural lung changes. Several studies have similarly shown that the relationship between smoking intensity and structural damage is not linear. For instance, some heavy smokers may exhibit minimal CT abnormalities, while others with modest exposure demonstrate advanced damage [12,13]. Host genetic factors, environmental exposures, and comorbid conditions likely mediate individual vulnerability to smoke-induced lung injury.

Comparison with Literature

The results of this study are consistent with international literature. Mets et al. [14] showed that young smokers with preserved lung function could already demonstrate CT-detectable emphysema, mirroring our finding of wider EI distribution among smokers. Nakano et al. [4] highlighted the role of AWT in predicting lung function decline, consistent with our observed trends. Sverzellati et al. [15] documented interstitial abnormalities in smokers, reinforcing the value of CT in early detection of subclinical disease. Tylen and Boijsen [16] also reported increased emphysema in asymptomatic smokers, comparable to our results. The systematic review by Dudurych et al. [9] further validated our finding of increased bronchial wall thickening in smokers.

Clinical Implications

The findings from this study carry significant clinical implications. First, HRCT is demonstrated as a sensitive tool capable of detecting subclinical structural changes in smokers before the onset of symptomatic disease. This may allow earlier risk stratification and preventive interventions. Second, quantitative CT indices such as AWT, EI, and lung volume could serve as imaging biomarkers for identifying high-risk individuals who may benefit from intensified smoking cessation strategies or closer clinical surveillance. Third, in LMICs such as India, where environmental risk factors overlap with smoking, HRCT may provide valuable differentiation between etiologies and guide targeted interventions.

Strengths and Limitations

A strength of this study is the inclusion of age-matched smokers and non-smokers, reducing confounding by age. Additionally, the use of standardized HRCT protocols and interobserver validation ensured reproducibility of findings. However, several limitations exist. The cross-sectional design prevents inference of causality or progression. The modest sample size reduced the ability to detect small but clinically important differences. Environmental exposures such as biomass fuel and air pollution were not controlled, which may have influenced non-smoker findings. Finally, radiation exposure remains a limitation in applying HRCT for large-scale screening.

Future Directions

Future research should involve longitudinal follow-up to assess progression from subclinical changes to overt COPD or emphysema. Larger, multi-center studies would enhance generalizability. Integration of HRCT with functional data such as spirometry, diffusion capacity, and exercise testing would provide more comprehensive disease characterization. The use of artificial intelligence and automated CT quantification could further enhance accuracy and reproducibility. Finally, correlating imaging biomarkers with molecular or serum markers of inflammation may yield deeper insights into disease mechanisms.

CONCLUSION

In conclusion, this study demonstrated that smokers exhibit trends toward increased airway wall thickness, greater bronchial remodeling, higher lung volumes, and heterogeneous emphysema distribution, even though differences did not achieve statistical significance. Correlation analysis highlighted strong interdependence between airway and parenchymal changes, underscoring the multifactorial nature of smoking-induced lung injury. While cumulative exposure measured in pack-years was a weak predictor, HRCT emerged as a sensitive modality for detecting early, subclinical changes. These findings reinforce the potential of HRCT to play a pivotal role in early detection, risk stratification, and preventive care for smokers at risk of developing chronic lung disease.

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