Trial reportBioMed research international2019
Development and Validation of a Prediction Pneumothorax Model in CT-Guided Transthoracic Needle Biopsy for Solitary Pulmonary Nodule.
Trial report in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Risk factors for the growth of ground-glass nodules in the lungs: A systematic review and meta-analysis.Clinics (Sao Paulo, Brazil) · 2025Pooled it
- Clinical and Procedural Predictive Model for Pneumothorax Risk After CT-Guided Lung Biopsy.Therapeutics and clinical risk management · 2026Article
- Clinical Variables and Radiomics Features for Predicting Pneumothorax in Patients Undergoing CT-guided Transthoracic Core Needle Biopsy.Radiology. Cardiothoracic imaging · 2024Article
- Article
- Relevant Risk Factor and Follow-Up of Lung Nodules in Physical Examination with Low-Dose CT Screening.Iranian journal of public health · 2023Article
- Development and validation of a prediction model of pneumothorax after CT-guided coaxial core needle lung biopsy.Quantitative imaging in medicine and surgery · 2022Article
- Development and Validation of a Random Forest Risk Prediction Pneumothorax Model in Percutaneous Transthoracic Needle Biopsy.Medical science monitor : international medical journal of experimental and clinical research · 2021Article
- MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule.Contrast media & molecular imaging · 2021Article
- Development of a hemoptysis risk prediction model for patients following CT-guided transthoracic lung biopsy.BMC pulmonary medicine · 2020Article
- The yield of immediate post lung biopsy CT in predicting iatrogenic pneumothorax.BMC pulmonary medicine · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomography-guided transthoracic needle biopsy (CT-TNB) is widely used in the diagnosis of solitary pulmonary nodule (SPN). However, CT-TNB-induced pneumothorax occurs frequently. This study aimed to establish a predictive model for pneumothorax following CT-TNB for SPN. The prediction model was developed in a cohort that consisted of 311 patients with SPN who underwent CT-TNB. An independent external validation cohort contained 227 consecutive patients. The least absolute shrinkage and selection operator (Lasso) regression analysis was used for data dimension reduction and predictors selection. Multivariable logistic regression was used to develop the predictive model, which was presented with a nomogram. Area under the curve (AUC) was used to determine the discrimination of the proposed model. The calibration was used to test the goodness-of-fit of the model, and decision curve analysis (DCA) was used for evaluating its clinical usefulness. Five variables (age, diagnosis of nodule, puncture times, puncture distance, and puncture position) were filtered by Lasso regression. AUC of the predictive model and the validation were 0.801 (95% CI, 0.738-0.865) and 0.738 (95% CI, 0.656-0.820), respectively. The model was well-calibrated (P > 0.05), and DCA demonstrated its clinical usefulness. Thus, this predictive model might facilitate the individualized preoperative prediction of pneumothorax in CT-TNB for SPN.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.