Development and validation of a random forest risk prediction pneumothorax model in percutaneous transthoracic needle biopsy

Abstract

Background: Computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) is an effective means for diagnosing various thoracic diseases. Pneumothorax is the most common complication, and when it becomes life-threatening, urgent medical intervention is required. The purpose of this study was to develop and validate a model that can be used to predict postoperative pneumothorax following CT-guided PTNB. Material/Methods: We enrolled 245 patients who completed CT-guided PTNB to develop the model. A random forest (RF) model was built using 15 risk factors (15-RFs). The 7 most critical risk factors (7-RFs) were extracted by feature selection and used to build a new model. The independent external validation data contained 97 patients. Logistic regression (LR), support vector machine (SVM), and decision tree (DT) models were also developed using both 15-RFs and 7-RFs, and their performance was compared with the RF models. Results: The length of the aerated lung traversed was identified as the most important risk factor for developing pneumothorax, followed by angle of pleural puncture, lesion depth, lesion size, age, procedure time, and sex. The RF model demonstrated better performance in the development and validation datasets when compared with the LR, SVM, and DT based on 15-RFs and 7-RFs. According to DeLong's test for difference in ROC curves, the RF models based on the 15-RFs and 7-RFs achieved similar classification performance (P>0.05). Conclusions: This study demonstrated the feasibility of using the 7-RFs RF model for predicting postoperative pneumothorax before patients undergo CT-guided PTNB.