Abstract
Background: The diagnosis of peripheral pulmonary nodules (PPNs) still is the key and difficult point. Previous studies have demonstrated that the diagnostic yield of radial endobronchial ultrasound (rEBUS) visible nodules is significantly higher than that of invisible nodules. The traditional method of predicting the rEBUS-visibility of nodules is based on the CT-bronchus signs, but its effectiveness may be unsatisfactory. Objective: We innovate a valuable predictive model based on virtual bronchoscopic navigation to identify beforehand which PPNs are likely to be successfully visualized by rEBUS. The innovative predictor is the ratio of the size of lesions (S) to the shortest straight-line distance (D) from the terminal point of the virtual navigation path to the localization point of the nodule. Methods: This is a retrospective study. On the training dataset of 214 patients, a receiver operating characteristic curve was drawn to understand the utility of the predictive model and get the optimal cut-off points. Ninety-two cases were enrolled in the validation dataset to validate the external predictive accuracy of the predictor. Results: The optimal cut-off point of the curve was 1.84 with the Youden index of 0.65, at which point the area under the curve was 0.85 (95% CI: 0.76-0.95). The predictor has a good performance in the validation dataset with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 81%, 100%, 100%, 71%, and 87%, respectively. Conclusion: The S/D ratio is a valuable and innovative method to identify beforehand which PPNs are likely to be successfully visualized by rEBUS. If the S/D ratio of the nodule is greater than 1.84, it will be visualized by rEBUS.