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.