{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:02:28Z","timestamp":1785301348676,"version":"3.55.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"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>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate a diagnostic model for malignant (MPE) and benign pleural effusion (BPE) using non-contrast chest CT deep learning (DL) and radiomics features, and to explore its incremental value alongside conventional biochemical biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively enrolled 208 patients (Jan 2020\u2013Sep 2024) as internal training\/testing cohorts (7:3 ratio) and 52 patients (Oct 2024\u2013Dec 2025) for internal temporal validation. Radiomics and DL features were extracted from non-contrast CTs to construct a radiomics score (Radscore). Multivariable logistic regression evaluated the Radscore\u2019s independent predictive value. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) assessed the incremental diagnostic value when combined with clinical biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Four radiomics features were ultimately retained to construct the Radscore. Multivariable adjusted analysis confirmed that the Radscore was an independent risk factor for MPE (OR: 2.718\u20132.776,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). The model demonstrated good predictive performance (AUC = 0.805\u20130.864). Exploratory subgroup analyses indicated consistent discriminative trends, though estimates in certain strata were inherently limited by small event counts. Correlation analysis revealed only a weak correlation between pleural effusion carcinoembryonic antigen (pCEA) and the Radscore (\n                      <jats:italic>r<\/jats:italic>\n                      = 0.270,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.001). Following the integration of pCEA, serum CEA (sCEA), pleural effusion adenosine deaminase (pADA), and serum CA125 (sCA125) into a combined clinical model, continuous NRI and IDI analyses demonstrated that the Radscore quantitatively refined individual risk estimation by shifting predicted probabilities in the appropriate direction for 75.0% of MPE patients and 66.7% of BPE patients.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The DL-radiomics-based Radscore is a promising quantitative biomarker for differentiating MPE from BPE. It functions independently of conventional biochemical metrics and provides meaningful incremental value, refining the accuracy of risk probability estimation in patients with pleural effusion.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1899111","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T04:35:03Z","timestamp":1785299703000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Incremental predictive value of a CT-based deep learning radiomics model for differentiating benign and malignant pleural effusions"],"prefix":"10.3389","volume":"13","author":[{"given":"Chun","family":"Cao","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, Xinghua People\u2019s Hospital Affiliated to Yangzhou University","place":["Xinghua, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Oncology, Xinghua People\u2019s Hospital Affiliated to Yangzhou University","place":["Xinghua, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Xinghua People\u2019s Hospital Affiliated to Yangzhou University","place":["Xinghua, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Oncology, Xinghua People\u2019s Hospital Affiliated to Yangzhou University","place":["Xinghua, 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