| Title: |
Multimodal prediction of persistent pulmonary nodules after COVID-19: radiomics feature integration with clinical and epidemiologic variables |
| Authors: |
Lijuan Ma; Hongyuan Xiao; Yonggang Huang; Ru Nan; Yulong Ma; Xinru Liang |
| Source: |
Frontiers in Medicine, Vol 13 (2026) |
| Publisher Information: |
Frontiers Media S.A. |
| Publication Year: |
2026 |
| Collection: |
Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: |
COVID-19; LASSO regression; nomogram; persistent nodules; predictive model; pulmonary nodules; Medicine (General); R5-920 |
| Description: |
BackgroundPersistent pulmonary nodules are increasingly identified in patients recovering from coronavirus disease 2019 (COVID-19). However, factors associated with long-term persistence remain insufficiently understood.ObjectiveTo 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.MethodsIn this single-center retrospective cohort study, 419 patients with newly detected pulmonary nodules after confirmed COVID-19 infection who had ≥ 6 months of follow-up were included (January 2020–December 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.ResultsAmong 419 patients, 210 (50.1%) had persistent nodules at 6 months. In age- and sex-adjusted analyses, ≥ 4 hospitalizations, prior tuberculosis, larger maximum nodule diameter (OR per mm increase: 1.121, 95% CI: 1.074–1.170), vascular convergence sign positivity, and ICU admission were associated with persistence. LASSO selected four key predictors, and multivariable analysis confirmed ≥ 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 50–83%.ConclusionThe proposed clinical–imaging model effectively identifies patients at higher risk of persistent pulmonary nodules after COVID-19 and may assist in optimizing individualized follow-up strategies. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fmed.2026.1777725/full; https://doaj.org/toc/2296-858X; https://doaj.org/article/159b191bf5c641cf9008d61afe7e3b22 |
| DOI: |
10.3389/fmed.2026.1777725 |
| Availability: |
https://doi.org/10.3389/fmed.2026.1777725; https://doaj.org/article/159b191bf5c641cf9008d61afe7e3b22 |
| Accession Number: |
edsbas.4968D218 |
| Database: |
BASE |