Risk prediction model for pneumothorax or pleural effusion after microwave ablation in patients with lung malignancy

Abstract

Background: Although microwave ablation (MWA) has been shown to be an effective treatment for lung malignancies (LM), there is no effective way to predict pneumothorax or pleural effusion after MWA so that timely measures can be taken to prevent it. Methods: This study comprised LM patients undergoing MWA at Affiliated Hospital of Nantong University from January 2013 to September 2023. Patients before May 2023 constituted the training set (n = 340), while data from May to September served as the test set (n = 58). Unformatted and formatted data extracted from electronic medical records (EMR) were utilized for model construction. Predictors for pneumothorax or pleural effusion were determined through univariate analysis and backward stepwise regression in the training set. Six ML algorithms were employed to create four models based on the research timeframe. Evaluation of the four models was performed using receiver operating characteristic (ROC) analysis, area under the ROC curve (AUC), and 10-fold cross validation. Findings: A total of 398 patients (216 aged 70 or above, 271 males) were included, with 23.37 % (93/398) experiencing pneumothorax and 33.42 % (133/398) developing pleural effusion. Across all four predictive models, Logistic Regression (LR) demonstrated optimal predictive performance in the test set, with AUC values of 0.727 for Model Ⅰ, 0.876 for Model Ⅱ, 0.895 for Model Ⅲ, and 0.807 for Model Ⅳ. Interpretation: ML models effectively predict post-MWA pneumothorax or pleural effusion.

Keywords

• Machine learning
• Pneumothorax
• Pleural effusion
• Microwave ablation
• Lung malignancy