Correlations between quantitative computed tomography metrics with serum fibrosis biomarkers, pulmonary function, and mortality risk in patients with idiopathic pulmonary fibrosis: a prospective cohort study

Introduction

Idiopathic pulmonary fibrosis (IPF) is an irreversible, chronic, progressive interstitial lung disease of unknown etiology (1). It is characterized by a gradual decline in pulmonary function that directly affects daily life and patient survival. The prevalence of IPF varies substantially by geographic region and is more frequently identified in older adults, with a median survival of 3–5 years (2). Pulmonary function assessment is essential for evaluating disease severity and for prognostic stratification in these patients (3).

The traditional pulmonary function evaluation for IPF includes spirometry to determine lung volumes and diffusing capacity testing (4). However, these examinations may be limited by insensitivity in early-stage disease, confounding by coexisting emphysema, measurement variability, and lack of specificity for fibrosis progression (5). In addition, the physical demands of testing and a patient’s ability or willingness to perform the required breathing maneuvers can lead to suboptimal results. The development of high-resolution computed tomography (HRCT) provides another option for evaluating pulmonary disease status. On HRCT, honeycomb-like changes with or without traction bronchiectasis and irregular thickening of the interlobular septa in a reticular pattern are typical findings and can provide diagnostic value in patients with IPF (6). With advances in software for HRCT image analysis, quantitative computed tomography (CT) can measure lung volume, interstitial density, extent of fibrosis, and tissue characteristics, enabling objective assessment of pulmonary function and disease progression. However, most previous studies were retrospective and used heterogeneous, nonstandardized reporting methods, such as visual assessment by radiologists with varying clinical backgrounds and experience (7,8). Additional studies are needed to confirm these relationships with pulmonary function and patient prognosis.

In addition to pulmonary function tests, some fibrosis biomarkers, such as hyaluronic acid, laminin, type IV collagen, and type III procollagen N-terminal peptide (PIIINP), have also been reported to be elevated in IPF patients (9). The levels of these fibrosis biomarkers not only indicate disease severity but also reflect ongoing disease activity. Whether quantitative CT images are correlated with these serum fibrosis biomarkers is unknown.

Therefore, we conducted this prospective study in IPF patients to evaluate the correlations between quantitative CT metrics and serum fibrosis biomarkers and pulmonary function tests, as well as the association between quantitative CT metrics and patient mortality. Our objective was to provide an evidence-based foundation to support individualized treatment strategies for IPF patients. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0962/rc).

Methods

Study design and participant selection

We conducted a prospective cohort study at the Affiliated Hospital of Hangzhou Normal University and Hangzhou Red Cross Hospital, Hangzhou, Zhejiang, China, between June 2018 and December 2022. The study protocol was approved by the ethics committee at each hospital (approval Nos. 2025086-001 and 2022(ES)-KS-052). All participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Inclusion criteria All IPF diagnoses were established by a multidisciplinary team (MDT) consisting of pulmonologists, chest radiologists, and pathologists. HRCT patterns were classified according to the 2018 ATS/ERS/JRS/ALAT guidelines as usual interstitial pneumonia (UIP), probable UIP, or indeterminate for UIP.were as follows: (I) age ≥18 years and (II) a diagnosis of IPF based on the 2018 guidelines published by ATS/ERS/JRS/ALAT (10). Patients were excluded if they had (I) secondary pulmonary fibrosis (e.g., environmental exposure to asbestosis or silicosis; connective tissue disease; drug-induced lung injury); (II) major coexisting organ dysfunction, including active pulmonary infection, bronchogenic carcinoma or metastatic lung tumors, liver disease with a Child-Pugh score ≥B (cirrhosis), kidney disease with an estimated glomerular filtration rate <30 mL/min/1.73 m², or cardiovascular disease (including congestive heart failure with American College of Cardiology class III–IV; (III) acute exacerbation of IPF; (IV) immunosuppressive therapy within 12 weeks before enrollment; (V) expected survival of <6 months; or (VI) pregnancy or breastfeeding.

Meanwhile, 30 healthy volunteers were recruited as a control group during the same period. All volunteers presented to the hospital for routine annual physical examinations.

Study protocol

Baseline information, including demographics, body weight, height, and smoking status, was collected. Fasting peripheral blood samples were obtained. Pulmonary fibrosis biomarkers, including hyaluronic acid, laminin, type IV collagen, and PIIINP, were measured by radioimmunoassay using an automated chemiluminescence immunoassay analyzer (i6000; Mindray Bio-Medical Electronics, China) in the hospital laboratory.

Pulmonary function tests were performed to measure forced vital capacity (FVC) and forced expiratory volume in one second (FEV1). The FEV1/FVC ratio was calculated. Pulmonary carbon monoxide diffusing capacity (DLCO) was measured to assess the efficiency of alveolar-capillary gas exchange.

Every participant underwent chest HRCT scanning using a 16-slice spiral CT scanner (Somatom Emotion; Siemens, Germany), with a slice thickness of 1.0–1.2 mm. We used the Volume Viewer image analysis system (GE Healthcare, USA) in conjunction with Vitrea post-processing software to quantitatively assess HRCT images. The primary metrics included the percentage of high-attenuation area (HAA%), mean CT value (MCT), and total lung volume (TLV). HAA% represented the extent of interstitial lesions and was defined as the percentage of TLV with CT values ≥−700 HU. MCT was defined as the average density of the entire lung, reflecting the degree of fibrosis and inflammation. TLV was automatically generated from pseudo-color images to estimate TLV and assess structural damage and volume changes.

All patients were followed in the clinic for 2 years, and deaths were recorded.

Statistical analysis

All statistical analyses were performed using SPSS software (version 26.0; IBM, United States). Continuous variables were tested for normality using the Shapiro-Wilk test. Normally distributed variables were presented as mean ± standard deviation and compared using the Student t-test. Non-normally distributed variables were presented as median (interquartile range) and compared using the Mann-Whitney U test. Categorical variables were presented as frequencies and percentages and compared using the chi-square test or Fisher’s exact test, as appropriate. Pearson correlation was used for correlation analyses. A Cox proportional hazards regression model was used to evaluate factors associated with mortality. A two-tailed P value <0.05 was considered statistically significant.

Results

Baseline characteristic of study participants

We enrolled 57 patients with IPF and 30 healthy controls. In the IPF group, there were 43 (75.4%) men and 14 (24.6%) women, with a mean age of 60.7 (±14.5) years. Disease duration ranged from 3 to 35 months, with a median duration of 10.6 months. Most patients (94.7%, 54 cases) were diagnosed based on clinical and imaging examinations, whereas the remaining 5.3% (3 cases) required pathological confirmation. Two patients received standard treatment with the antifibrotic drug nintedanib before enrollment, without prior use of glucocorticoids or immunomodulators (such as cyclophosphamide and azathioprine). None of the patients reported a history of occupational dust exposure. The control group had 30 healthy adults, including 21 males and 9 females, with a mean age of 58.5 (±16.2) years old. There were no statistically significant differences in age, sex, body mass index, and smoking history between the two groups (P>0.05) (Table 1).

Comparisons of quantitative CT measurements between IPF patients and healthy adults

Compared with healthy adults, patients with IPF had statistically significantly higher HAA% and MCT but lower TLV (Figure 1). Figure 2 illustrates greater lung tissue fibrosis in a patient with IPF than in a healthy adult.

Figure 1 Quantitative CT images showed that IPF patients showed statistically significantly higher HAA% and MCT, but lower TLV measurements than healthy adults. **, P<0.01. CT, computed tomography; HAA%, percentage of high-attenuation area; HU, Hounsfield unit; IPF, idiopathic pulmonary fibrosis; MCT, mean CT value; TLV, total lung volume.

Figure 2 Quantitative analysis of high-resolution computed tomography images in Volume Viewer image analysis system (GE Healthcare, USA). Segmentation at a threshold of −700 HU, where red represents fibrotic tissue and blue indicates normal lung tissue. (A) healthy adult; (B) a patient with idiopathic pulmonary fibrosis. HU, Hounsfield unit.

Correlation analyses of quantitative CT measurements with serum markers and pulmonary function test results

HAA% showed significant positive correlations with hyaluronic acid, type IV collagen, laminin, and PIIINP and a negative correlation with FVC, FEV1, and DLCO (Table 2). MCT showed significant positive correlations with hyaluronic acid, type IV collagen, laminin, and PIIINP and a negative correlation with FVC. LV showed significant negative correlations with hyaluronic acid, laminin, and PIIINP and positive correlations with FVC and FEV1.

Cox regression survival analysis

During 2-year follow-up period, 25 (43.9%) patients died. The Cox proportional hazards regression model showed that age, HAA%, and MCT were risk factors for mortality, whereas TLV was a protective factor (Table 3). We also performed the multivariate Cox regression analysis. Since MCT, TLV, FVC, and DLCO all reflect disease severity in IPF, simultaneous inclusion of them in one model could lead to multicollinearity and model overfitting in this relatively small cohort. We performed two multivariate Cox regression analyses (Tables 4,5). Both analyses showed that HAA% provided prognostic information beyond conventional pulmonary function evaluation.

Discussion

Evaluating pulmonary function in patients with IPF is important for establishing baseline disease severity, monitoring disease progression, guiding treatment decisions, and determining prognosis (11). In the current study, we showed that quantitative CT image metrics were correlated with serum fibrosis biomarkers and pulmonary function test results. In addition, these quantitative CT measurements, together with age, could predict mortality. Our findings provide an evidence-based approach to evaluating disease severity and activity and may support personalized management in patients with IPF.

Traditional pulmonary function tests include spirometry (measuring FVC and FEV1), lung volume measurements (measuring total lung capacity and residual volume), and DLCO. However, these tests, such as spirometry and DLCO, can be effort-dependent, requiring patients to inhale maximally and then perform a forceful, blast-like exhalation to measure FVC, or to hold their breath for exactly 10 s to obtain accurate DLCO results (5,12). These maneuvers can be especially difficult for patients with IPF. The tests can also trigger vigorous coughing, which can invalidate the results (13).

Quantitative CT is a noninvasive, rapid test that requires minimal patient effort. It can provide an objective, reproducible assessment of disease extent and progression in IPF, correlates strongly with pulmonary function tests, and offers independent prognostic information for mortality and disease progression (14). Among quantitative CT metrics, HAA% represents the fractional lung volume with increased density and can serve as a marker of fibrotic burden, correlating with physiologic impairment. A higher HAA% reflects more parenchymal abnormalities, such as ground-glass opacities and reticular structures, greater physiologic restriction, and more extensive fibrotic disease (15). The American Thoracic Society and Fleischner Society have recognized HAA% as a common metric for assessing severity and monitoring progression in interstitial lung disease (16). MCT is calculated as the mean attenuation and reflects the overall density of the lung parenchyma, providing a global measure of disease burden (17). Lung volume is commonly used to assess abnormalities in lung tissues such as the bronchi and pulmonary interstitium. TLV measured by quantitative CT is the anatomic equivalent of total lung capacity from traditional tests such as body plethysmography or gas dilution (18). In our study, we showed that IPF patients had increased HAA% and MCT and reduced TLV compared to healthy adults. This was consistent with the restrictive lung injury from the pathological fibrosis formation in the IPF patients. In addition, we found that HAA% had significant negative correlations with FVC, FEV1, and DLCO. MCT showed a significant negative correlation with FVC. LV showed significant positive correlations with FVC and FEV1. All of these results were in agreement with previous study reports, confirming that quantitative HRCT can be applied for evaluating fibrosis severity in UPF patients (17,19,20).

Pulmonary fibrosis is closely related to lung collagen content. Serum biomarkers such as hyaluronic acid, laminin, type IV collagen, and PIIINP are correlated with pulmonary fibrosis severity and patient prognosis (21). Hyaluronic acid is a key extracellular matrix component synthesized by myofibroblasts in the lung interstitium. During the development of IPF, hyaluronic acid can accumulate and create a profibrotic environment that drives lung fibrosis (22).

Laminin belongs to a family of multifunctional macromolecules and is found ubiquitously in basement membranes. Laminin is important for maintaining alveolar epithelial integrity. Dysregulation of laminin can trigger a cascade of pathological processes, including activation of the intra-alveolar coagulation cascade, imbalance of matrix metalloproteinases and their inhibitors, and myofibroblast activation, leading to pathological fibrosis and reduced elasticity (23). Type IV collagen is another major component of the alveolar basement membrane and is essential for maintaining structural support and regulating cell behavior in the lung. During pulmonary fibrosis, overproduction of collagen and increased crosslinking can severely reduce gas diffusion and oxygen exchange (24). PIIINP is a cleavage product released during type III collagen synthesis in the lung interstitial matrix. Its level correlates with fibroblast activation and fibrogenesis (25).

Compared with pulmonary function tests, which require patient cooperation to complete the examinations, these biomarkers can be detected in peripheral blood samples or alveolar fluid. These biomarkers reflect not only fibrosis severity but also dynamic disease activity (26). In the current study, HAA% and MCT measured by quantitative CT showed significant positive correlations with hyaluronic acid, type IV collagen, laminin, and PIINP, whereas TLV showed significant negative correlations with hyaluronic acid, laminin, and PIIP. These results are consistent with previous reports and suggest that quantitative HRCT can be applied not only to evaluate fibrosis severity but also to estimate disease activity in UPF patients.

Previous studies have shown that serum biomarker levels and pulmonary function test results are associated with patient mortality (27). Higher levels of serum biomarkers, including hyaluronic acid, collagen type IV, laminin, and PIIINP, as well as poor pulmonary function, are significantly correlated with high mortality in patients with IPF (26). In our study, high levels of HAA% and MCT, as well as a low TLV level, were also associated with a high mortality rate in IPF patients during the 2-year follow-up period. In addition, age was associated with mortality. Therefore, we performed a multivariate Cox regression analysis, which showed that age, HAA%, and MCT were risk factors for death, whereas LV was a protective factor. These results indicate that quantitative CT measurements could be used as prognostic indicators for patients with IPF, although other patient characteristics, such as age, should also be considered. These measurements may facilitate clinical disease-severity stratification and risk prediction, providing an evidence-based rationale for treatment decisions.

Overall, our study showed that quantitative CT metrics were significantly associated with serum fibrosis biomarkers and pulmonary function measurements, indicating that they could reflect both the biological activity and functional consequences of fibrosis in IPF. Compared with healthy controls, IPF patients exhibited increased HAA% and MCT and decreased TLV, consistent with greater fibrotic burden and loss of functional lung volume. Furthermore, HAA%, MCT, and TLV were significantly associated with mortality, suggesting that quantitative CT metrics could have prognostic value in addition to assessing IPF severity. After adjusting for FVC and DLCO, HAA% still showed independent prognostic value, suggesting that quantitative CT could capture distinct aspects of IPF pathology, such as the spatial distribution and fibrotic tissue density that are not fully reflected by the global pulmonary function test. This could be particularly relevant for patients who cannot perform reliable spirometry due to dyspnea or cough, where quantitative CT may serve as an alternative risk stratification tool. All this evidence supported the potential role of quantitative CT as a noninvasive and objective tool for evaluating disease burden, risk stratification, and prognosis in this patient population.

The strengths of this study included its long follow-up period for observing mortality in patients with IPF. We showed that quantitative CT metrics could be used to evaluate both disease severity and fibrosis activity in patients with IPF. However, this study was limited by a small sample size and its single-center design. During the quantitative CT imaging analysis, we used ≥−700 HU as the threshold to define the high-attenuation area and to distinguish normal lung tissue from fibrotic lesions. Differences in threshold selection may lead to different assessments of CT quantitative measurements. Therefore, future multicenter studies with larger sample sizes are needed to optimize threshold classification criteria based on IPF subtypes and to systematically validate the clinical application of quantitative CT measurements to provide accurate disease severity assessment and prognostic risk stratification in patients with IPF. In addition, we did not consider the impact of treatment on patient mortality when evaluating the association between CT measurement and patient mortality risk. Future studies incorporating longitudinal monitoring of CT measurement and detailed treatment course are required to further delineate the role of quantitative CT scan in predicting patient mortality risk.

Conclusions

In conclusion, quantitative CT measurements were correlated with serum fibrotic biomarkers (hyaluronic acid, type IV collagen, laminin, and PIIINP) and pulmonary function test results (FVC, FEV1, and DLCO) and could facilitate the assessment of disease severity and activity, as well as the prediction of mortality risk, in patients with IPF.

Acknowledgments

We thank Medjaden Inc. for the scientific editing of this manuscript.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0962/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0962/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0962/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0962/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the ethics committees of Affiliated Hospital of Hangzhou Normal University and Hangzhou Red Cross Hospital (approval Nos. 2025086-001 and 2022(ES)-KS-052). All participants provided written informed consent.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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