{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T22:37:22Z","timestamp":1773268642783,"version":"3.50.1"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"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>Background<\/jats:title>\n                    <jats:p>Persistent pulmonary nodules are increasingly identified in patients recovering from coronavirus disease 2019 (COVID-19). However, factors associated with long-term persistence remain insufficiently understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To determine whether a predictive model integrating clinical and CT imaging features can estimate the risk of pulmonary nodule persistence at 6 months after COVID-19.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this single-center retrospective cohort study, 419 patients with newly detected pulmonary nodules after confirmed COVID-19 infection who had \u2265 6 months of follow-up were included (January 2020\u2013December 2024). Clinical and computed tomography (CT) features were collected. Predictors were selected using least absolute shrinkage and selection operator (LASSO) regression and incorporated into a multivariable logistic regression model. Model performance was assessed using receiver operating characteristic curves and calibration analysis. Internal validation was performed using 1,000 bootstrap resamples to estimate optimism-corrected performance. Decision curve analysis was also conducted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 419 patients, 210 (50.1%) had persistent nodules at 6 months. In age- and sex-adjusted analyses, \u2265 4 hospitalizations, prior tuberculosis, larger maximum nodule diameter (OR per mm increase: 1.121, 95% CI: 1.074\u20131.170), vascular convergence sign positivity, and ICU admission were associated with persistence. LASSO selected four key predictors, and multivariable analysis confirmed \u2265 4 hospitalizations, prior tuberculosis, larger nodule diameter, and vascular convergence sign as independent risk factors. The model achieved an AUC of 0.728, with bootstrap-corrected AUC of 0.717. Decision curve analysis demonstrated clinical net benefit within threshold probabilities of 50\u201383%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed clinical\u2013imaging model effectively identifies patients at higher risk of persistent pulmonary nodules after COVID-19 and may assist in optimizing individualized follow-up strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1777725","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T06:50:25Z","timestamp":1773211825000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal prediction of persistent pulmonary nodules after COVID-19: radiomics feature integration with clinical and epidemiologic 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