Interstitial lung abnormalities (ILAs) are incidental findings in computed tomography (CT) images of the chest, indicating early-stage interstitial lung disease (ILD) in individuals without a formal clinical diagnosis [1]. Unlike advanced clinical and subclinical forms of ILD, ILAs are typically identified during imaging performed for unrelated medical reasons [1]. With the increasing use of CT for diverse diagnostic purposes, the detection rate of ILAs is anticipated to increase. Previous studies have demonstrated a highly variable prevalence of ILAs across populations, ranging from 3% to 17% (Table 1) [2–6].

Numerous studies have demonstrated the importance of early detection of ILAs, linking them to respiratory symptoms such as chronic cough and breathlessness, reduced lung function, diminished exercise tolerance, and an increased risk of all-cause mortality [5–10]. In addition, ILAs can progress to more severe conditions, including pulmonary fibrosis [9,11]. Despite their clinical significance, they are frequently underreported in radiology reports, even within academic institutions [12]. This review aims to provide clinical practitioners with a concise overview of ILAs by integrating current knowledge on diagnostic criteria, clinical relevance, and management strategies.

According to the Fleischner Position Paper, ILAs are radiologically defined as incidental CT findings of non-dependent lung parenchymal abnormalities involving at least 5% of any lung zone in individuals without prior suspicion of ILD [1]. These abnormalities include ground-glass opacities (GGOs), a reticular pattern, lung distortion, traction bronchiectasis, honeycombing, and non-emphysematous cysts [1] (Fig. 1). The 5% threshold is used to exclude minimal abnormalities and ensure that only significant findings are considered. Focal or unilateral GGOs, focal or unilateral reticulations, and patchy GGOs involving less than 5% of the lung are categorized as equivocal [1,3,13]. In addition, certain imaging findings are not indicative of ILAs, including dependent lung atelectasis, focal paraspinal fibrosis, smoking-related centrilobular nodularity without accompanying features, interstitial edema (e.g., in heart failure), and aspiration-related findings such as patchy GGOs or a tree-in-bud pattern (Fig. 2) [1].

ILAs are categorized based on their distribution and the presence of fibrosis (Fig. 3). These categories include non-subpleural ILAs, which do not have a predominant sub-pleural location; subpleural non-fibrotic ILAs, which have a predominant subpleural location but no fibrosis; and sub-pleural fibrotic ILAs, which have a subpleural location and features of pulmonary fibrosis, such as architectural distortion, traction bronchiectasis or bronchiolectasis, and honeycombing [1]. The presence of subpleural fibrotic ILAs is strongly associated with a higher risk of disease progression and mortality [7,14].

Differentiating ILAs from clinical and subclinical ILDs requires a thorough clinical evaluation, as the presence of ILAs does not preclude respiratory symptoms or functional impairment. Moreover, abnormalities identified during targeted screening for ILD in high-risk populations–such as individuals with connective tissue disease or a family history of ILD–are categorized as subclinical or preclinical ILD, rather than ILAs, due to their non-incidental nature.

The evaluation of ILAs should include a dedicated chest CT with thin slices (< 1.5 mm) and moderate edge-enhancing reconstruction [1]. High-resolution CT (HRCT) is recommended when the initial scan is incomplete or yields equivocal findings [1,15]. Prone imaging is particularly useful for distinguishing dependent atelectasis from interstitial abnormalities, whereas expiratory imaging aids the identification of lobular air trapping, which may suggest conditions such as hypersensitivity pneumonitis [16,17].

The identification of ILAs has conventionally been the responsibility of experienced radiologists or pulmonologists, relying on visual assessment [2,5–7,13,18]. Although these techniques provide high inter-reader agreement and reliability, they are time-intensive and can lead to discrepancies in distinguishing ILAs from equivocal cases [19]. For instance, a study of 336 health check-up participants revealed discordant ILA classifications between two readers in 49 cases (14.6%). Among these, 42.9% (21 of 49) involved mild, non-dependent lung abnormalities that one reader categorized as ILA and the other considered equivocal [19].

To overcome the challenges associated with conventional methods of identifying ILAs, automated quantification systems employing computational techniques have been developed to enhance accuracy and efficiency [19–24]. These methods include densitometry, local histogram analysis, and advanced artificial intelligence-based texture evaluation, which are effective for quantifying subtle changes in the lung parenchyma [20,22,23,25,26]. Several studies have compared the performance of automated quantitative methods with radiologist assessments in detecting ILAs [19,20,22]. For example, Kim et al. [19] utilized a deep learning-based quantitative system, demonstrating a sensitivity of 67.6%, specificity of 93.3%, and accuracy of 90.5% for identifying ILAs, using a threshold of 5% lung parenchymal abnormalities in at least one lung zone. Similarly, Chae et al. [20] found that deep learning-based texture analysis detected a 4% prevalence of ILAs in the Korean general population. This method achieved 100% sensitivity and 99% specificity, using a lower threshold of 1.8% abnormalities, suggesting the potential for improved accuracy with reduced thresholds. Despite their advantages in reducing clinician workload and mitigating subjective bias, automated quantification systems have limitations. False positives are common, often resulting from suboptimal inspiration or inconsistencies in imaging protocols across vendors, underscoring the need to standardize imaging procedures [19,22].

The development and refinement of quantitative tools for ILA assessment highlight their potential for integration into clinical practice. These tools provide a standardized approach to evaluation, enabling consistent assessments across multiple institutions and facilitating multicenter studies as well as longitudinal monitoring. However, the thresholds and parameters employed by these systems require further optimization to enhance diagnostic accuracy.

Recent studies have demonstrated significant variability in the prevalence of ILAs across populations. They are observed in 4–17% of smokers and lung cancer screening cohorts [4,5,13,27,28] and 3–10% of population-based cohorts [2,24,27,29,30]. Furthermore, ILAs have a lower prevalence in Asian populations compared to Western populations. For instance, Tsushima et al. [29] reported a prevalence of 2.6% in the Japanese general population, whereas Chae et al. [20] identified a prevalence of 4% among participants in the Korean National Lung Cancer Screening Program. Conversely, another study reported a 10% prevalence of ILAs in a Western lung cancer screening cohort. The variability in ILA prevalence can be attributed to several factors, including differences in cohort characteristics such as mean age, genetic predispositions, environmental exposures, and imaging protocols. In Lee et al. [6] and Chae et al. [20], which involved Korean cohorts, the use of low-dose chest CT for health or lung cancer screening may have contributed to the lower prevalences observed. Furthermore, the MUC5B promoter polymorphism, a well-recognized risk factor for idiopathic pulmonary fibrosis (IPF), is rare in the South Korean population and may partly explain the regional differences in prevalence [31]. Inconsistent definitions of ILAs further complicate prevalence estimates. For example, Tsushima et al. [1,29] defined ILAs as interstitial changes, including honeycombing, interlobular septal thickening, GGOs, ill-defined subpleural lines, and combined pulmonary fibrosis and emphysema, which differs from definitions used in recent studies. The Fleischner Society has provided guidelines to standardize ILA definitions, which have reduced variability in the reported prevalence rates. However, differences in imaging techniques and interpretation contribute to the wide range of prevalence estimates.

Advanced age is a key risk factor for ILAs, with multiple studies consistently reporting a higher prevalence among older individuals [3,18,22,32]. Data from the Framingham Heart Study (FHS) demonstrated a significant increase in ILA prevalence, increasing from 4% in individuals under 60 years to 47% in those over 70 years of age [32]. Sex differences in ILA prevalence have been observed, with some studies suggesting that ILAs are more common in men than women [13,27]. However, these findings are inconsistent, with other studies reporting no significant differences [3,5,18,30]. Smoking is also a significant risk factor for ILAs, with current smokers and those with greater cumulative exposure (e.g., higher pack-years) exhibiting an increased likelihood of ILA development [13,27,33]. For example, in the FHS and Age, Gene/Environment Susceptibility (AGES)–Reykjavik cohorts, Putman et al. [27] found that the ILA group had a higher proportion of current smokers and greater smoking exposure compared to the non-ILA group. However, the association between smoking and ILA presence is not consistently observed across studies. Lee et al. [3] found no significant differences in smoking status or intensity between the ILA and non-ILA groups (ever smokers: 88.3% vs. 90.3%, p = 0.433; smoking quantity: 39.4 vs. 38.4 pack-years, p = 0.692). Importantly, ILAs have a prevalence of 7.9% even among never smokers, underscoring the need for vigilance in identifying these abnormalities regardless of smoking history [34].

Environmental and occupational exposures are significant contributors to the risk of developing ILAs. Salisbury et al. [9] demonstrated that specific environmental exposures were independently associated with the presence of ILAs in a multivariable model adjusted for age, smoking status, MUC5B genotype, and telomere restriction fragment length. These exposures included aluminum smelting (odds ratio [OR] 14.88, 95% confidence interval [CI] 2.67–97.73; p = 0.005), lead (OR 2.91, 95% CI 1.05–8.05; p = 0.04), birds (OR 3.37, 95% CI 1.53–7.41; p = 0.003), and mold (OR 3.83, 95% CI, 1.78–8.25; p = 0.001). Similarly, Sack et al. [35], which involved the Multi-Ethnic Study of Atherosclerosis (MESA) cohort, reported that exposure to vapors, gas, dust, and fumes was associated with a 2.64% increase in high-attenuation areas (95% CI 1.23–4.19%). In addition, self-reported exposure to vapor or gas was linked to a 1.97-fold higher likelihood of ILAs among currently employed individuals (95% CI 1.16–3.35).

The association between ambient air pollution and ILAs has been highlighted in several recent studies [36–38]. Although definitive evidence remains limited, air pollutants such as particulate matter (PM), ozone, nitrogen oxide, sulfur dioxide, and carbon monoxide contribute to pulmonary inflammation and alveolar epithelial injury. These pollutants can induce oxidative stress through the production of reactive oxygen species, including hydroxyl radicals and superoxide anions, as well as promote telomere shortening, which can promote these pathophysiological processes [36]. The MESA Air-Lung Study found a significant association between exposure to nitrogen oxides, a marker of traffic-related air pollution, and the presence of ILAs (OR 1.77 per 40 ppb, 95% CI 1.06–2.95; p = 0.03) [37]. Similarly, in population-based FHS cohorts, an interquartile range difference in 5-year exposure to elemental carbon, a component of fine particulate matter (PM_2.5) from traffic emissions, was associated with a 1.27-fold increased likelihood of ILAs (95% CI 1.04–1.55) [38].

Genetic predisposition plays a crucial role in the development of ILAs [18]. The most consistent genetic risk factor for ILA is an increased copy number of a common variant (rs35705950) in the promoter region of the MUC5B gene, which is strongly associated with IPF [2,7,18,30,39,40]. In the MESA longitudinal cohort study, the MUC5B promoter single nucleotide polymorphism (rs35705950) was significantly associated with incident ILA (hazard ratio [HR] 1.73, 95% CI 1.17–2.56; p = 0.01) [18]. This association has been corroborated in multiple cohorts, including the FHS [2,30], COPDGene [40], and AGES–Reykjavik studies [7], where each copy of the variant was associated with a 1.5- to 2.7-fold increased risk of ILAs. Recent genome-wide association studies have further identified novel genetic associations specific to ILAs, such as variants near IPO11 (rs6886640) and FCF1P3 (rs73199442), which are not linked to IPF [40]. These findings underscore both the overlap and distinctions in genetic risk factors between ILAs and IPF, suggesting that ILAs may represent a spectrum of early ILDs.

Structured approaches to the management and follow-up of patients with ILAs are crucial to reduce the risk of disease progression and improve clinical outcomes. Upon identification of ILA, a comprehensive clinical evaluation is needed. This includes obtaining a detailed patient history, focusing on symptoms such as chronic cough, dyspnea, and exposure to risk factors such as smoking and occupational hazards [1,15]. Pulmonary function tests should be conducted to assess baseline lung function, with particular attention to parameters such as FVC and diffusion capacity for carbon monoxide [1,15]. These investigations can identify clinically significant ILD, indicating the extent of functional impairment and guiding further management.

Serial chest imaging is crucial for monitoring ILAs. HRCT with thin sections is needed to assess changes in lung parenchyma over time. It is essential to maintain standardized imaging protocols to ensure consistency and comparability of results [1]. Radiologists should focus on identifying features indicative of progression, such as increased reticulation, honeycombing, and traction bronchiectasis [15]. Quantitative CT analysis can be used to objectively assess the extent and severity of ILAs, helping to detect subtle changes that may indicate progression [51].

Monitoring and management strategies for ILAs should be individualized according to the patient’s risk profile and disease characteristics (Fig. 4). Factors such as fibrotic features on radiological imaging, including architectural distortion with traction bronchiectasis or honeycombing and subpleural basal predominance, the presence of the MUC5B promoter polymorphism, and patient demographics (e.g., age, smoking status, and environmental exposures) may influence the likelihood of progression to ILD [1,43]. In cases where ILAs show extensive fibrosis with impaired lung function or are associated with significant respiratory symptoms, referral to an ILD specialist for further evaluation and appropriate treatment, such as antifibrotics, should be considered. Once the possibility of ILD has been ruled out, ILAs should be stratified according to their risk factors for progression. Individuals without risk factors should be monitored until the onset of respiratory symptoms or other signs of disease progression [1,15], whereas those with risk factors should be followed regularly every 12–24 months or sooner if new respiratory symptoms occur or lung function declines [1,15]. However, the optimal interval and duration of follow-up remain unclear. Park et al. [51] suggested that a follow-up CT every 3 years may be appropriate for individuals with initially detected ILA, whereas those at high risk due to extensively fibrotic ILA and honeycombing require more frequent CT examinations. If ILA progression is suspected, the decision to perform diagnostic procedures such as video-assisted thoracoscopic surgery-assisted biopsy, bronchoalveolar lavage, or cryobiopsy should be individualized based on clinical presentation and HRCT findings. Patients with fibrotic ILA exhibiting basal and peripheral distribution are at an increased risk of progression and may have subclinical ILDs, which makes lung biopsy potentially helpful for diagnosing IPF or specific ILDs [43]. However, in cases where HRCT reveals a usual interstitial pneumonia pattern and the multidisciplinary team categorizes it as subclinical IPF, invasive tests may not be necessary [43].

A multidisciplinary approach involving radiologists, pulmonologists, and primary care providers is crucial for the effective management and follow-up of ILAs [43]. In addition, lifestyle modifications, such as smoking cessation and vaccination against respiratory infections, are essential components in managing ILA. By combining comprehensive clinical assessment, standardized imaging protocols, and evidence-based risk stratification, healthcare providers can improve the prognosis and prevent progression to severe forms of ILD. Further studies and the integration of novel diagnostic tools, including advanced imaging techniques and new biomarkers, are needed to enhance management strategies and clinical outcomes.

This review emphasizes the clinical significance of ILAs, which are incidental findings in chest CT images that may indicate early ILDs. Although the prevalence of ILAs varies across different populations, their detection is crucial due to their potential risk of progression to severe lung conditions, such as fibrosis, and their association with increased mortality. Advances in imaging technology, particularly automated quantitative systems, have significantly enhanced the accuracy of ILA assessments, enabling early and accurate detection. Effective management requires a multidisciplinary approach tailored to individual risk profiles to mitigate disease progression and improve outcomes. Further studies are needed to refine diagnostic criteria, enhance imaging techniques, and develop targeted therapies to better understand and treat ILA.