{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T05:41:54Z","timestamp":1776231714069,"version":"3.50.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"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>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a combined clinical and tumor\u2013peritumoral CT radiomics model to differentiate bronchiolar adenoma (BA) from lung adenocarcinoma (LUAD), thereby improving preoperative diagnostic accuracy and guiding individualized treatment strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 362 patients with pathologically confirmed BA or LUAD were retrospectively analyzed. Data from Medical Center 1 (n = 281) were divided into training and test sets (7:3 ratio), and data from Medical Center 2 (n = 81) served as an external validation set. Clinical characteristics, CT morphological features, and tumor\u2013peritumoral radiomics features were extracted. Five machine learning algorithms were applied to construct and compare predictive models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Lung lobe distribution, density, vacuolar sign, tumor-associated vessels, distance to pleura, and nodule diameters differed significantly between BA and LUAD. Among radiomics models, the tumor\u2013peritumoral MLP model achieved the best performance (AUCs: 0.918, 0.912, 0.888). The clinical\u2013radiomics fusion model outperformed single models, with AUCs of 0.935, 0.939, and 0.910 and accuracies of 0.862, 0.847, and 0.864 in the training, test, and validation sets, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed fusion model enables accurate, non-invasive differentiation between BA and LUAD, offering valuable support for personalized clinical decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1798218","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:42:14Z","timestamp":1776228134000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A fusion model based on tumor and peritumoral CT radiomics for differentiating bronchiolar adenoma from lung 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