ArticleBMC pulmonary medicine2021
Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules.
Article in BMC pulmonary medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Incidence and risk factors for malignancy in patients with incidental solitary pulmonary nodules: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Pooled it
- Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis.Journal of cardiothoracic surgery · 2022Pooled it
- Low-dose versus standard-dose computed tomography-guided biopsy for pulmonary nodules: a randomized controlled trial.Journal of cardiothoracic surgery · 2023Trial
- Development and validation of the Pulmonary Nodule Malignant Transformation Fear Scale (PN-MTFS) to identify patients at high risk of cancer-related fear: a multicenter study.Frontiers in psychology · 2026Article
- Enhanced Malignancy Prediction of Small Lung Nodules in Different Populations Using Transfer Learning on Low-Dose Computed Tomography.Diagnostics (Basel, Switzerland) · 2025Article
- CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.Frontiers in oncology · 2025Article
- Predictive model of malignancy probability in pulmonary nodules based on multicenter data.Frontiers in oncology · 2025Article
- Establishment and verification of a prediction model based on clinical characteristics and computed tomography radiomics parameters for distinguishing benign and malignant pulmonary nodules.Journal of thoracic disease · 2024Article
- Article
- Preoperative computed tomography-guided localization for multiple pulmonary nodules: comparison of methylene blue and coil.Journal of cardiothoracic surgery · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs. MATERIALS AND
methodsBetween January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified.
resultsIn the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = - 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828-0.913) than the other two models.
conclusionsWe established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.
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