{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T15:44:32Z","timestamp":1793807072483,"version":"4.6.1"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>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).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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 (<jats:italic>n<\/jats:italic> = 207) and a test cohort (<jats:italic>n<\/jats:italic> = 90). Radiomic features were extracted from CT images of lung fibrosis lesions in the training cohort. These features were reduced in dimensionality to construct morphological biomarkers. ML methods were then used to develop three models: a clinical model, a radiomics model, and a multimodal joint model. The performance of these models was evaluated in the test cohort using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The multimodal model, combining CT radiomics and clinical features, offers an effective and accurate tool for predicting the progression of pulmonary fibrosis in CWP.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1599739","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T05:31:10Z","timestamp":1753162270000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis"],"prefix":"10.3389","volume":"12","author":[{"given":"Xiaobing","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuemei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongsheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wutao","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"210250","DOI":"10.1183\/16000617.0250-2021","article-title":"Understanding the pathogenesis of occupational coal and silica dust-associated lung disease.","volume":"31","author":"Vanka","year":"2022","journal-title":"Eur Respir Rev."},{"key":"B2","doi-asserted-by":"publisher","first-page":"805","DOI":"10.5858\/arpa.2022-0491-OA","article-title":"Pathologic findings in severe coal workers\u2019 pneumoconiosis in contemporary US coal miners.","volume":"148","author":"Cool","year":"2024","journal-title":"Arch Pathol Lab Med."},{"key":"B3","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1097\/JOM.0000000000002795","article-title":"Survival and disease burden analysis of occupational pneumoconiosis from 1956 to 2021 in Jiangsu Province.","volume":"65","author":"Duan","year":"2023","journal-title":"J Occup Environ Med."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1186\/s12889-023-16295-2","article-title":"Global incidence, prevalence and disease burden of silicosis: 30 years\u2019 overview and forecasted trends.","volume":"23","author":"Liu","year":"2023","journal-title":"BMC Public Health."},{"key":"B5","doi-asserted-by":"publisher","first-page":"88690","DOI":"10.1007\/s11356-022-21966-5","article-title":"Prevalence of coal worker\u2019s pneumoconiosis: A systematic review and meta-analysis.","volume":"29","author":"Liu","year":"2022","journal-title":"Environ Sci Pollut Res Int."},{"key":"B6","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.clinimag.2023.02.010","article-title":"Imaging diagnosis of pneumoconiosis with predominant nodular pattern: HRCT and pathologic findings.","volume":"97","author":"Akira","year":"2023","journal-title":"Clin Imaging."},{"key":"B7","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s00428-024-03845-1","article-title":"The role of pathologists in the diagnosis of occupational lung diseases: An expert opinion of the European society of pathology pulmonary pathology working group.","volume":"485","author":"Calabrese","year":"2024","journal-title":"Virchows Arch."},{"key":"B8","doi-asserted-by":"publisher","first-page":"108232","DOI":"10.1016\/j.pharmthera.2022.108232","article-title":"Progressive massive fibrosis: An overview of the recent literature.","volume":"240","author":"Weissman","year":"2022","journal-title":"Pharmacol Ther."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1513\/AnnalsATS.202109-1064OC","article-title":"Pathology and mineralogy demonstrate respirable crystalline silica is a major cause of severe pneumoconiosis in U.S. coal miners.","volume":"19","author":"Cohen","year":"2022","journal-title":"Ann Am Thorac Soc."},{"key":"B10","doi-asserted-by":"publisher","first-page":"L600","DOI":"10.1152\/ajplung.00310.2021","article-title":"From pulmonary fibrosis to progressive pulmonary fibrosis: A lethal pathobiological jump.","volume":"321","author":"Selman","year":"2021","journal-title":"Am J Physiol Lung Cell Mol Physiol."},{"key":"B11","doi-asserted-by":"publisher","first-page":"101191","DOI":"10.1016\/j.mam.2023.101191","article-title":"Mechanisms of organ fibrosis: Emerging concepts and implications for novel treatment strategies.","volume":"92","author":"Lurje","year":"2023","journal-title":"Mol Aspects Med."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1161\/CIRCRESAHA.121.319971","article-title":"The latest in animal models of pulmonary hypertension and right ventricular failure.","volume":"130","author":"Boucherat","year":"2022","journal-title":"Circ Res."},{"key":"B13","doi-asserted-by":"publisher","first-page":"e00080","DOI":"10.1161\/HHF.0000000000000080","article-title":"Pulmonary hypertension in congenital heart disease: A scientific statement from the American heart association.","volume":"16","author":"Jone","year":"2023","journal-title":"Circ Heart Fail."},{"key":"B14","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s10653-024-02114-z","article-title":"Research progress on the pathogenesis and prediction of pneumoconiosis among coal miners.","volume":"46","author":"Hang","year":"2024","journal-title":"Environ Geochem Health."},{"key":"B15","doi-asserted-by":"publisher","first-page":"911","DOI":"10.3390\/medicina57090911","article-title":"Challenges in diagnosing occupational chronic obstructive pulmonary disease.","volume":"57","author":"Libu","year":"2021","journal-title":"Medicina (Kaunas)."},{"key":"B16","doi-asserted-by":"publisher","first-page":"5811","DOI":"10.3390\/jcm10245811","article-title":"Early diagnosis and real-time monitoring of regional lung function changes to prevent chronic obstructive pulmonary disease progression to severe emphysema.","volume":"10","author":"Jung","year":"2021","journal-title":"J Clin Med."},{"key":"B17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1097\/RLI.0000000000001103","article-title":"CT Quantification of interstitial lung abnormality and interstitial lung disease: From technical challenges to future directions.","volume":"60","author":"Choe","year":"2025","journal-title":"Invest Radiol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"5164970","DOI":"10.1155\/2022\/5164970","article-title":"Modern diagnostic imaging technique applications and risk factors in the medical field: A review.","volume":"2022","author":"Hussain","year":"2022","journal-title":"Biomed Res Int."},{"key":"B19","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.ymeth.2020.05.022","article-title":"Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.","volume":"188","author":"Ibrahim","year":"2021","journal-title":"Methods."},{"key":"B20","doi-asserted-by":"publisher","first-page":"19062","DOI":"10.1038\/s41598-023-46391-7","article-title":"Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules.","volume":"13","author":"Selvam","year":"2023","journal-title":"Sci Rep."},{"key":"B21","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.3390\/diagnostics14111103","article-title":"Revolutionizing cardiology through artificial intelligence-big data from proactive prevention to precise diagnostics and cutting-edge treatment-A comprehensive review of the past 5 years.","volume":"14","author":"Stamate","year":"2024","journal-title":"Diagnostics (Basel)."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.3390\/pharmaceutics16101328","article-title":"Artificial Intelligence (AI) applications in drug discovery and drug delivery: Revolutionizing personalized medicine.","volume":"16","author":"Serrano","year":"2024","journal-title":"Pharmaceutics."},{"key":"B23","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1177\/23971983211064463","article-title":"Diagnosis and monitoring of systemic sclerosis-associated interstitial lung disease using high-resolution computed tomography.","volume":"7","author":"Khanna","year":"2022","journal-title":"J Scleroderma Relat Disord."},{"key":"B24","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1177\/0284185119857435","article-title":"Highlights of high-resolution computed tomography imaging in evaluation of complications and co-morbidities in idiopathic pulmonary fibrosis.","volume":"61","author":"Ricci","year":"2020","journal-title":"Acta Radiol."},{"key":"B25","doi-asserted-by":"publisher","first-page":"S7","DOI":"10.1097\/RTI.0000000000000705","article-title":"Recent advancements in computed tomography assessment of fibrotic interstitial lung diseases.","volume":"38","author":"Suman","year":"2023","journal-title":"J Thorac Imaging."},{"key":"B26","doi-asserted-by":"publisher","first-page":"17534666241282538","DOI":"10.1177\/17534666241282538","article-title":"The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis.","volume":"18","author":"Chen","year":"2024","journal-title":"Ther Adv Respir Dis."},{"key":"B27","doi-asserted-by":"publisher","first-page":"86","DOI":"10.3348\/kjr.2020.0144","article-title":"Spectral computed tomography: Fundamental principles and recent developments.","volume":"22","author":"So","year":"2021","journal-title":"Korean J Radiol."},{"key":"B28","doi-asserted-by":"publisher","first-page":"2956","DOI":"10.1111\/1541-4337.12958","article-title":"Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review.","volume":"21","author":"Du","year":"2022","journal-title":"Compr Rev Food Sci Food Saf."},{"key":"B29","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s12989-022-00449-y","article-title":"Coal dust exposure triggers heterogeneity of transcriptional profiles in mouse pneumoconiosis and Vitamin D remedies.","volume":"19","author":"Mu","year":"2022","journal-title":"Part Fibre Toxicol."},{"key":"B30","doi-asserted-by":"publisher","first-page":"2201","DOI":"10.1038\/s41598-020-77924-z","article-title":"A deep learning-based model for screening and staging pneumoconiosis.","volume":"11","author":"Zhang","year":"2021","journal-title":"Sci Rep."},{"key":"B31","doi-asserted-by":"publisher","first-page":"uiad017","DOI":"10.1093\/joccuh\/uiad017","article-title":"Artificial intelligence in advancing occupational health and safety: An encapsulation of developments.","volume":"66","author":"Shah","year":"2024","journal-title":"J Occup Health."},{"key":"B32","doi-asserted-by":"publisher","first-page":"e10694","DOI":"10.1002\/btm2.10694","article-title":"Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers.","volume":"9","author":"Sanchez-Morillo","year":"2024","journal-title":"Bioeng Transl Med."},{"key":"B33","doi-asserted-by":"publisher","first-page":"6417","DOI":"10.1111\/jcmm.16621","article-title":"Mesenchymal stem cells ameliorate silica-induced pulmonary fibrosis by inhibition of inflammation and epithelial-mesenchymal transition.","volume":"25","author":"Wei","year":"2021","journal-title":"J Cell Mol Med."},{"key":"B34","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1111\/crj.13657","article-title":"Efficient clinical data analysis for prediction of coal workers\u2019 pneumoconiosis using machine learning algorithms.","volume":"17","author":"Dong","year":"2023","journal-title":"Clin Respir J."},{"key":"B35","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1186\/s12880-021-00723-z","article-title":"Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning.","volume":"21","author":"Yang","year":"2021","journal-title":"BMC Med Imaging."},{"key":"B36","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1164\/rccm.202112-2684OC","article-title":"Deep learning-based outcome prediction in progressive fibrotic lung disease using high-resolution computed tomography.","volume":"206","author":"Walsh","year":"2022","journal-title":"Am J Respir Crit Care Med."},{"key":"B37","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1097\/RLI.0000000000000974","article-title":"Artificial intelligence and interstitial lung disease: Diagnosis and prognosis.","volume":"58","author":"Dack","year":"2023","journal-title":"Invest Radiol."},{"key":"B38","doi-asserted-by":"publisher","first-page":"e208","DOI":"10.1016\/j.crad.2021.11.006","article-title":"Prospective machine learning CT quantitative evaluation of idiopathic pulmonary fibrosis in patients undergoing anti-fibrotic treatment using low- and ultra-low-dose CT.","volume":"77","author":"Koo","year":"2022","journal-title":"Clin Radiol."},{"key":"B39","doi-asserted-by":"publisher","first-page":"357","DOI":"10.3390\/diagnostics13030357","article-title":"Research progress of respiratory disease and idiopathic pulmonary fibrosis based on artificial intelligence.","volume":"13","author":"Zhang","year":"2023","journal-title":"Diagnostics (Basel)."},{"key":"B40","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.3390\/cancers14071654","article-title":"Using quantitative imaging for personalized medicine in pancreatic cancer: A review of radiomics and deep learning applications.","volume":"14","author":"Preuss","year":"2022","journal-title":"Cancers (Basel)."},{"key":"B41","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s00330-023-10016-4","article-title":"An automatic texture feature analysis framework of renal tumor: Surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.","volume":"34","author":"Yang","year":"2024","journal-title":"Eur Radiol."},{"key":"B42","doi-asserted-by":"publisher","first-page":"3125","DOI":"10.3390\/diagnostics12123125","article-title":"Stability and reproducibility of radiomic features based on various segmentation techniques on cervical cancer DWI-MRI.","volume":"12","author":"Ramli","year":"2022","journal-title":"Diagnostics (Basel)."},{"key":"B43","doi-asserted-by":"publisher","first-page":"19594","DOI":"10.1038\/s41598-022-22877-8","article-title":"Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions.","volume":"12","author":"Tharmaseelan","year":"2022","journal-title":"Sci Rep."},{"key":"B44","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1002\/med.21846","article-title":"A review in radiomics: Making personalized medicine a reality via routine imaging.","volume":"42","author":"Guiot","year":"2022","journal-title":"Med Res Rev."},{"key":"B45","doi-asserted-by":"publisher","first-page":"5921","DOI":"10.3390\/cancers13235921","article-title":"Applications of radiomics and radiogenomics in high-grade gliomas in the era of precision medicine.","volume":"13","author":"Fathi Kazerooni","year":"2021","journal-title":"Cancers (Basel)."},{"key":"B46","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.dld.2022.12.015","article-title":"Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma.","author":"Wei","year":"2023","journal-title":"Dig Liver Dis."},{"key":"B47","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.1080\/17476348.2024.2311262","article-title":"A three-gene random forest model for diagnosing idiopathic pulmonary fibrosis based on circadian rhythm-related genes in lung tissue.","volume":"17","author":"He","year":"2023","journal-title":"Expert Rev Respir Med."},{"key":"B48","doi-asserted-by":"publisher","first-page":"1433393","DOI":"10.3389\/fimmu.2024.1433393","article-title":"Lactylation signature identifies liver fibrosis phenotypes and traces fibrotic progression to hepatocellular carcinoma.","volume":"15","author":"Li","year":"2024","journal-title":"Front Immunol."},{"key":"B49","doi-asserted-by":"publisher","first-page":"135","DOI":"10.11152\/mu-2746","article-title":"Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images.","volume":"23","author":"Constantinescu","year":"2021","journal-title":"Med Ultrason."},{"key":"B50","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1186\/s12905-024-03231-8","article-title":"Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer.","volume":"24","author":"Song","year":"2024","journal-title":"BMC Womens Health."},{"key":"B51","doi-asserted-by":"publisher","first-page":"3846","DOI":"10.3390\/jcm13133846","article-title":"Developing the benchmark: Establishing a gold standard for the evaluation of AI caries diagnostics.","volume":"13","author":"Boldt","year":"2024","journal-title":"J Clin Med."},{"key":"B52","doi-asserted-by":"publisher","first-page":"6867","DOI":"10.1007\/s00330-020-07030-1","article-title":"Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment.","volume":"30","author":"Deniffel","year":"2020","journal-title":"Eur Radiol."},{"key":"B53","doi-asserted-by":"publisher","first-page":"5930","DOI":"10.1007\/s00330-022-08776-6","article-title":"Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma.","volume":"32","author":"Chu","year":"2022","journal-title":"Eur Radiol."},{"key":"B54","doi-asserted-by":"publisher","first-page":"e6947","DOI":"10.1002\/cam4.6947","article-title":"Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.","volume":"13","author":"Tan","year":"2024","journal-title":"Cancer Med."},{"key":"B55","doi-asserted-by":"publisher","first-page":"108006","DOI":"10.1016\/j.cmpb.2024.108006","article-title":"Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis.","volume":"244","author":"Liu","year":"2024","journal-title":"Comput Methods Programs Biomed."},{"key":"B56","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1186\/s12931-024-03004-9","article-title":"Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients.","volume":"25","author":"Li","year":"2024","journal-title":"Respir Res."},{"key":"B57","doi-asserted-by":"crossref","first-page":"11065","DOI":"10.3390\/ijerph191711065","article-title":"Workers\u2019 occupational dust exposure and pulmonary function assessment: Cross-sectional study in China.","volume":"19","author":"He","year":"2022","journal-title":"Int J Environ Res Public Health."},{"key":"B58","doi-asserted-by":"publisher","first-page":"e70042","DOI":"10.1111\/ctr.70042","article-title":"Outcomes of lung transplantation in coal workers pneumoconiosis: Analysis of UNOS database.","volume":"38","author":"Nandavaram","year":"2024","journal-title":"Clin Transplant."},{"key":"B59","doi-asserted-by":"publisher","first-page":"122459","DOI":"10.1016\/j.envpol.2023.122459s","article-title":"The understudied global experiment of pollution\u2019s impacts on wildlife and human health: The ethical imperative for interdisciplinary research.","volume":"336","author":"de Jersey","year":"2023","journal-title":"Environ Pollut."}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1599739\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T05:31:10Z","timestamp":1753162270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1599739\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,22]]},"references-count":59,"alternative-id":["10.3389\/fmed.2025.1599739"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2025.1599739","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,22]]},"article-number":"1599739"}}