ArticleFrontiers in medicine2025
Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Global research landscape versus disease burden in pneumoconiosis diagnosis: a dual-source bibliometric analysis [1999-2025].Journal of thoracic disease · 2026Article
- Development of a clinical nomogram for predicting hemorrhagic rupture in renal angiomyolipoma and analysis of molecular correlates (Open medicine (Warsaw, Poland) · 2026Article
- Associations of inflammation-related hematological profile with the early-stages of cardiovascular-kidney-metabolic syndrome and the mediating role of body composition: evidence from the China National Health Survey.Diabetology & metabolic syndrome · 2025Article
- Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.Frontiers in medicine · 2025Article
- Development of a machine learning-based risk prediction model for early-stage pneumoconiosis: a retrospective study.Frontiers in medicine · 2025Article
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10 authors.
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Abstract
Objective: This study aims to develop a machine learning (ML) model that integrates computed tomography (CT) radiomics with clinical features to predict the progression of pulmonary interstitial fibrosis in patients with coalworker pneumoconiosis (CWP). Methods: Clinical and imaging data from 297 patients diagnosed with CWP at The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College between December 2021 and December 2023 were analyzed. Of these patients, 170 developed pulmonary interstitial fibrosis over a 3-year follow-up and were classified as the progression group, while 127 patients showed stable conditions and were classified as the stable group. The patients were divided into a training cohort ( Results: In the training cohort, the area under the curve (AUC) for the clinical, radiomics, and joint models were 0.835, 0.879, and 0.945, respectively. In the test cohort, the AUC values for these models were 0.732, 0.750, and 0.845, respectively. The joint model demonstrated the highest predictive performance and clinical benefit in both the training and test cohorts. Conclusion: The multimodal model, combining CT radiomics and clinical features, offers an effective and accurate tool for predicting the progression of pulmonary fibrosis in CWP.
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