{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:09:58Z","timestamp":1760058598506,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015083","name":"Research and Development Foundation of Peking University People\u2019s Hospital","doi-asserted-by":"publisher","award":["RDL 2022-06"],"award-info":[{"award-number":["RDL 2022-06"]}],"id":[{"id":"10.13039\/501100015083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Current Oncology"],"abstract":"<jats:p>Objectives: The preoperative identification of occult pleural metastasis (OPM) in lung cancer remains a crucial clinical challenge. This study aimed to develop and validate a predictive model that integrates clinical information with chest CT radiomic features to preoperatively identify patients at risk of OPM. Methods: This study included 50 patients diagnosed with OPM during surgery as the positive training cohort and an equal number of nonmetastatic patients as the negative control cohort. Using least absolute shrinkage and selection operator (LASSO) logistic regression, we identified key radiomic features and calculated radiomic scores. A predictive nomogram was developed by combining clinical characteristics and radiomic scores, which was subsequently validated with data from an additional 545 patients across three medical centers. Results: Univariate and multivariate logistic regression analyses revealed that carcinoembryonic antigen (CEA), the neutrophil-to-lymphocyte ratio (NLR), the clinical T stage, and the tumor\u2013pleural relationship were significant clinical predictors. The clinical model alone achieved an area under the curve (AUC) of 0.761. The optimal integrated model, which combined radiomic scores from the volume of interest (VOI) with the CEA and NLR, demonstrated an improved predictive performance, with AUCs of 0.890 in the training cohort and 0.855 in the validation cohort. Conclusions: Radiomic features derived from CT scans show significant promise in identifying patients with lung cancer at risk of OPM. The nomogram developed in this study, which integrates CEA, the NLR, and radiomic tumor area scores, enhances the precision of preoperative OPM prediction and provides a valuable tool for clinical decision-making.<\/jats:p>","DOI":"10.3390\/curroncol32040223","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T06:45:31Z","timestamp":1744353931000},"page":"223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Computed Tomography-Based Radiomic Nomogram to Predict Occult Pleural Metastasis in Lung Cancer"],"prefix":"10.3390","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6646-0953","authenticated-orcid":false,"given":"Xiaoyi","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery, Peking University People\u2019s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China"},{"name":"Thoracic Oncology Institute, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Peking University People\u2019s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China"},{"name":"Thoracic Oncology Institute, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kongxu","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Peking University People\u2019s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China"},{"name":"Thoracic Oncology Institute, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyu","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Peking University People\u2019s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China"},{"name":"Thoracic Oncology Institute, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Peking University People\u2019s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China"},{"name":"Thoracic Oncology Institute, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Peking University People\u2019s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China"},{"name":"Thoracic Oncology Institute, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanchao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Peking University People\u2019s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China"},{"name":"Thoracic Oncology Institute, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. 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