A Diagnostic Predictive Model of Bronchoscopy with Radial Endobronchial Ultrasound for Peripheral Pulmonary Lesions

Takayasu Ito, Yuji Matsumoto, Shotaro Okachi, Kazuki Nishida, Midori Tanaka, Tatsuya Imabayashi, Takaaki Tsuchida, Naozumi Hashimoto

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Background: Several factors have been reported to affect the diagnostic yield of bronchoscopy with radial endobronchial ultrasound (R-EBUS) for peripheral pulmonary lesions (PPLs). However, it is difficult to accurately predict the diagnostic potential of bronchoscopy for each PPL in advance. Objectives: Our objective was to establish a predictive model to evaluate the diagnostic yield before the procedure. Method: We retrospectively analysed consecutive patients who underwent diagnostic bronchoscopy with R-EBUS between April 2012 and October 2015. We assessed the factors that were predictive of successful bronchoscopic diagnosis of PPLs with R-EBUS using a multivariable logistic regression model. The accuracy of the predictive model was evaluated using the receiver operator characteristic area under the curve (ROC AUC). Internal validation was analysed using 10-fold stratified cross-validation. Results: We analysed a total of 1,634 lesions; the median lesion size was 25.0 mm. Of these, 1,138 lesions (69.6%) were successfully diagnosed. In the predictive logistic model, significant factors affecting the diagnostic yield were lesion size, lesion structure, bronchus sign, and visible on chest X-ray. The predictive model consisted of seven factors: lesion size, lesion lobe, lesion location from the hilum, lesion structure, bronchus sign, visibility on chest X-ray, and background lung. The ROC AUC of the predictive model was 0.742 (95% confidence interval: 0.715-0.769). Internal validation using 10-fold stratified cross-validation revealed a mean ROC AUC of 0.734. Conclusions: The predictive model using the seven factors revealed a good performance in estimating the diagnostic yield.

Original languageEnglish
Pages (from-to)1148-1156
Number of pages9
Issue number12
Publication statusPublished - 01-12-2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Pulmonary and Respiratory Medicine


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