ArticleWorld journal of radiology2026
Development and validation of a clinical factor-based nomogram for predicting imaging-defined cardiopulmonary abnormality risk in an asymptomatic screening population.
Article in World journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundChronic 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 cardiopulmonary imaging abnormalities in asymptomatic individuals.
aimTo 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.
methodsA 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 (
resultsUnivariate 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 (
conclusionThe 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.
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