{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T16:34:16Z","timestamp":1788280456131,"version":"build-2803163510"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"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. Oncol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Malignant pleural effusion (MPE) is a common clinical problem that requires cytological and\/or histological confirmation obtained by invasive examination to establish a definitive diagnosis. Radiomics is rapidly evolving and can provide a non-invasive tool to identify MPE.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>We aimed to develop a model based on radiomic features extracted from unenhanced chest computed tomography (CT) images and investigate its value in predicting MPE.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>This retrospective study included patients with pleural effusions between January 2016 and June 2020. All patients underwent a chest CT scanning and medical thoracoscopy after artificial pneumothorax. Cases were divided into a training cohort and a test cohort for modelling and verifying respectively. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) were applied to determine the optimal features. We built a radiomics model based on support vector machines (SVM) and evaluated its performance using ROC and calibration curve analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Twenty-nine patients with MPE and fifty-two patients with non-MPE were enrolled. A total of 944 radiomic features were quantitatively extracted from each sample and reduced to 14 features for modeling after selection. The AUC of the radiomics model was 0.96 (95% CI: 0.912-0.999) and 0.86 (95% CI: 0.657~1.000) in the training and test cohorts, respectively. The calibration curves for model were in good agreement between predicted and actual data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The radiomics model based on unenhanced chest CT has good performance for predicting MPE and may provide a powerful tool for doctors in clinical decision-making.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2024.1419343","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T04:43:35Z","timestamp":1723437815000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["The value of computed tomography-based radiomics for predicting malignant pleural effusions"],"prefix":"10.3389","volume":"14","author":[{"given":"Zhen-Chuan","family":"Xing","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua-Zheng","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi-Liang","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Xia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1183\/16000617.0019-2016","article-title":"Malignant pleural effusion: from bench to bedside","volume":"25","author":"Psallidas","year":"2016","journal-title":"Eur Respir Rev"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1136\/thx.2010.137018","article-title":"Local anesthetic thoracoscopy: British Thoracic Society Pleural Disease Guideline 2010","author":"Rahman","year":"2010","journal-title":"Thorax"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2016.11.010","article-title":"US hospitalizations for Malignant pleural effusions: data from the 2012 national inpatient sample","volume":"151","author":"Taghizadeh","year":"2017","journal-title":"Chest"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1097\/CM9.0000000000002108","article-title":"Cancer statistics in China and United States, 2022: profiles, trends, and determinants","volume":"135","author":"Xia","year":"2022","journal-title":"Chin Med J"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1136\/thx.2010.136994","article-title":"Management of a Malignant pleural effusion: British Thoracic Society Pleural Disease Guideline 2010","author":"Roberts","year":"2010","journal-title":"Thorax"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.201902-189CME","article-title":"Contemporary approach to the diagnosis of Malignant pleural effusion","volume":"16","author":"Kaul","year":"2019","journal-title":"Ann Am Thorac Soc"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21552","article-title":"Artificial intelligence in cancer imaging: Clinical challenges and applications","volume":"69","author":"Bi","year":"2019","journal-title":"CA: Cancer J Clin"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer (Oxford England: 1990)"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.154.3.2106209","article-title":"CT in differential diagnosis of diffuse pleural disease","volume":"154","author":"Leung","year":"1990","journal-title":"AJR Am J Roentgenol"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2014-206054","article-title":"Role of CT in assessing pleural Malignancy prior to thoracoscopy","volume":"70","author":"Hallifax","year":"2015","journal-title":"Thorax"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.21873\/anticanres.14054","article-title":"Diagnosis of Malignant pleural effusion using CT scan and pleural-fluid cytology together. 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