ArticleThe Thoracic and cardiovascular surgeon2025
A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.
Article in The Thoracic and cardiovascular surgeon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Dexmedetomidine Inhibits Ferroptosis to Alleviate Hypoxia/Reoxygenation-Induced Cardiomyocyte Injury by Regulating the HDAC2/FPN Pathway.Cardiovascular drugs and therapy · 2026Article
- Advantages of integrating artificial intelligence and spectral CT for lung nodule classification and prognostic judgment: a narrative review.Journal of thoracic disease · 2025Review
- Advances in modelling the risk of benign and malignant lung nodules.Frontiers in oncology · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundLung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrates artificial intelligence (AI) analysis with biomarkers to enhance early detection and stratification of lung nodule malignancy.
methodsThe study retrospectively analyzed the patients with pathologically confirmed pulmonary nodules. AI technology was employed to assess CT features, such as nodule size, solidity, and malignancy probability. Additionally, lung cancer blood biomarkers were measured. Statistical analysis involved univariate analysis to identify significant differences among factors, followed by multivariate logistic regression to establish independent risk factors. The model performance was validated using receiver operating characteristic curves and decision curve analysis (DCA) for internal validation. Furthermore, an external dataset comprising 51 cases of lung nodules was utilized for independent validation to assess robustness and generalizability.
resultsA total of 176 patients were included, divided into benign/preinvasive (
conclusionThis combined approach of AI-based CT features analysis with lung cancer biomarkers provides a more accurate and clinically useful tool for guiding treatment decisions in pulmonary nodule patients. Further studies with larger cohorts are warranted to validate these findings across diverse patient populations.
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