ArticleJournal of cardiothoracic surgery2024
Non-malignant pathological results from CT-guided biopsy for pulmonary nodules: a predictive model for identifying false-negative results.
Article in Journal of cardiothoracic surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Computed tomography-guided biopsy for pulmonary nodules: comparison between with and without coaxial technique.Journal of cardiothoracic surgery · 2026Article
- Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach.Discover oncology · 2026Article
- Predictive value of CT target scan-based radiomics and clinical features for patients with chronic obstructive pulmonary disease combined with malignant pulmonary nodules.Biomedical engineering online · 2026Article
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5 authors.
Funding
Abstract
backgroundComputed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, it is critical to correctly determine false-negative results. Therefore, the current study was designed to construct a predictive model for predicting false-negative cases among patients receiving CTB for PNs who receive non-malignant results. MATERIALS AND
methodsFrom January 2016 to December 2020, consecutive patients from two centers who received CTB-based non-malignant pathology results while undergoing evaluation for PNs were examined retrospectively. A training cohort was used to discover characteristics that predicted false negative results, allowing the development of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy.
resultsThe training cohort included 102 patients with PNs who showed non-malignant pathology results based on CTB. Each patient underwent CTB for a single nodule. Among these patients, 85 and 17 patients, respectively, showed true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUV
conclusionsIn patients with PNs, the predictive model developed herein demonstrated good diagnostic effectiveness for identifying false-negative CTB-based non-malignant pathology data.
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