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A predictive model for diagnosing peripheral pulmonary lesions using radial probe endobronchial ultrasound images
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A predictive model for diagnosing peripheral pulmonary lesions using radial probe endobronchial ultrasound images
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Journal Article
A predictive model for diagnosing peripheral pulmonary lesions using radial probe endobronchial ultrasound images
2025
Overview
Radial probe endobronchial ultrasound transbronchial lung biopsy with guide sheath (RP-EBUS-GS-TBLB) was one of the main diagnostic methods for peripheral pulmonary lesions (PPLs). The aim of this study was to develop a predictive model for the diagnostic rate of RP-EBUS-TBLB in PPLs. A total of 189 consecutive patients with PPLs who had undergone RP-EBUS-TBLB between January 2022 and October 2024 in 8th Medical Centre, Chinese PLA General Hospital were enrolled in this retrospective single-center cohort study. The LASSO regression method was used to select predictors and nomogram model was developed using multivariate logistic regression. Internal validation was performed using bootstrapping. Model performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Bootstrapping method was applied for internal validation. The diagnostic rate of RP-EBUS-TBLB in PPLs was 74.07% (140/189). Six (lesion morphology in CT, number of biopsies, size, margin, echogenicity and RP-EBUS location) variables were selected by the LASSO regression analysis. We applied EBUS imaging features (size, margin, echogenicity and RP-EBUS location; model 1) separately and combined them with clinical features (lesion morphology in CT and number of Biopsies; model 2) to develop two predictive models. The AUC of model 1 was 0.889 (95% CI, 0.826–0.943), and it was 0.917 (95% CI, 0.862–0.960) in model 2. The predictive model was well calibrated and DCA indicated its potential clinical usefulness. However, there is no significant difference in AUC between the two models, which suggest that the model 1(only using EBUS imaging features) can serve as a concise and efficient predictive model and has great potential to predict the diagnostic rate of RP-EBUS-TBLB in PPLs.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
