{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T12:34:31Z","timestamp":1767875671837,"version":"3.49.0"},"reference-count":37,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T00:00:00Z","timestamp":1608076800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Laboratory Analysis"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Accurate prediction of malignancy risk for pulmonary lesions with pleural effusion improves early diagnosis of lung cancer. This study aimed to develop and validate a model to predict lung cancer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Clinical data of 536 patients with pulmonary diseases were collected. The risk factors were identified by regression analysis. Three prediction models were developed. The predictive performances of the models were measured by the area under the curves (AUCs) and calibrated with 1000 bootstrap samples to minimize the over\u2010fitting bias. The net benefits of the models were evaluated by decision curve analysis. Finally, a separate cohort of 134 patients was used to validate the models externally.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Seven independent risk factors were identified from 18 clinical variables, which included the pleural fluid carcinoembryonic antigen (CEA), serum cytokeratin\u201019 fragment (CYFRA 21\u20101), the ratio of CEA in the pleural fluid to serum, extrathoracic cancer history (&gt;5\u00a0years), tumor size, vessel convergence, and lobulation. The AUCs of the three models were 0.976, 0.927, and 0.944 in the training set and 0.930, 0.845, and 0.944 in the external set,\u00a0respectively. The accuracies of the three models were 89.6%, 81.4%, and 88.8%. Model 1 showed the best iteration fit (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>\u00a0=\u00a00.84, 0.68, and 0.73) and a higher net benefit on decision curve analysis when compared to the other two models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The advantageous model could assess the risk of lung cancer in patients with pleural effusion and act as a useful tool for early identification of lung cancer.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcla.23682","type":"journal-article","created":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T21:55:30Z","timestamp":1608155730000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Development of risk prediction models for lung cancer based on tumor markers and radiological signs"],"prefix":"10.1002","volume":"35","author":[{"given":"Yuqin","family":"Tu","sequence":"first","affiliation":[{"name":"Department of Medical Laboratory The First Affiliated Hospital Chongqing Medical University Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Blood Transfusion The First Affiliated Hospital Chongqing Medical University Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunfeng","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital Chongqing Medical University Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyun","family":"Bi","sequence":"additional","affiliation":[{"name":"Department of Medical Laboratory The First Affiliated Hospital Chongqing Medical University Chongqing 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