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

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:

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

Exclusion criteria:

1. Pre-existing chronic respiratory diseases (COPD, asthma, interstitial lung disease).
2. Occupational lung disease or prior thoracic surgery.
3. History of pulmonary malignancy.
4. 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)

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

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