Revised: June 23, 2026
Accepted: July 8, 2026
Published online: July 28, 2026
Processing time: 53 Days and 22.4 Hours
Chronic cardiopulmonary diseases, including chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease, represent a major global health burden. Low-dose computed tomography (LDCT) combined with artificial intelligence (AI) quantitative imaging enables the identification of car
To evaluate early risk factors for cardiopulmonary imaging abnormalities in asymptomatic middle-aged and elderly population using single-inspiratory phase LDCT combined with AI-based whole-lung quantitative analysis.
A retrospective collection was conducted on 1035 asymptomatic individuals aged ≥ 40 years who underwent routine single-inspiratory-phase LDCT screening at Zhuzhou 331 Hospital in 2025. An AI platform was utilized to automatically extract airway wall area percentage, low-attenuation area percentage, interstitial lung abnormality, and coronary artery calcification score. Based on risk stratification criteria, the population was divided into a high-risk group (n = 689) and a low-risk group (n = 346) and randomly stratified into a training set (n = 724) and a validation set (n = 311) at a 7:3 ratio. Independent sample t-test or χ2 test was applied to analyze between-group differences. Binary logistic regression was used to screen independently associated factors, and a multivariate logistic regression model was constructed after excluding collinear variables using variance inflation factor (VIF < 5), followed by the establishment of a nomogram prediction model. The receiver operating characteristic curve and DeLong test were employed to evaluate model discrimination. The Hosmer-Lemeshow test and calibration curves were used to assess goodness of fit. Decision curve analysis (DCA) was applied to evaluate clinical net benefit, and internal validation was performed using the bootstrap method (1000 resamplings).
Univariate analysis showed that smoking history, abnormal metabolic status, body mass index (BMI), age, and gender were significantly associated with cardiopulmonary imaging-defined high-risk status (P < 0.05), while work style showed a marginal association in univariate analysis (P = 0.043) but did not retain statistical significance in the multivariate model (P = 0.851). After VIF collinearity screening (all VIF < 5) and multivariate logistic regression analysis with forced entry of six candidate variables, smoking history [odds ratio (OR) = 1.968 per level, 95% confidence interval (CI): 1.665-2.325, P < 0.001], abnormal metabolic status (OR = 3.266, 95%CI: 2.259-4.723, P < 0.001), BMI (OR = 1.150 per unit, 95%CI: 1.079-1.226, P < 0.001), and age (OR = 1.028 per year, 95%CI: 1.005-1.052, P = 0.018) were identified as independently associated factors for cardiopulmonary imaging-defined high-risk status, while male gender demonstrated an (inverse) association (OR = 0.427, 95%CI: 0.276-0.660, P < 0.001) after ad
The LDCT-based nomogram model combined with AI quantitative imaging analysis may assist in identifying individuals with cardiopulmonary imaging abnormalities in asymptomatic screening populations, potentially guiding further diagnostic evaluation. Prospective studies are warranted to establish its role in improving clinical outcomes.
Core Tip: This study developed and validated a clinical factor-based nomogram for predicting imaging-defined cardiopulmonary imaging abnormalities in 1035 asymptomatic screening individuals using artificial intelligence-assisted low-dose computed tomography quantitative analysis. Five independently associated factors were identified: Smoking history, abnormal metabolic status, body mass index, age, and gender. The nomogram achieved an area under the curve of 0.833 in the validation set. This model may assist early, non-invasive risk stratification for cardiopulmonary imaging abnormalities during routine lung cancer screening, potentially guiding timely preventive interventions in high-risk populations.