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To develop effective nomograms for predicting pneumothorax and delayed pneumothorax after microwave ablation (MWA) in lung malignancy (LM) patients. LM patients treated with MWA were randomly allocated to a training or validation cohort at a ratio of 7:3. The predictors of pneumothorax identified by univariate and multivariate analyses in the training cohort were used to develop a predictive nomogram. The C-statistic was used to evaluate predictive accuracy in both cohorts. A second nomogram for predicting delayed pneumothorax was developed and validated using identical methods. A total of 552 patients (training cohort: n = 402; validation cohort: n = 150) were included; of these patients, 27.9% (154/552) developed pneumothorax, with immediate and delayed pneumothorax occurring in 18.8% (104/552) and 9.1% (50/552), respectively. The predictors selected for the nomogram of pneumothorax were emphysema (hazard ratio [HR], 6.543; p <.001), history of lung ablation (HR, 7.841; p=.025), number of pleural punctures (HR, 1.416; p <.050), ablation zone encompassing pleura (HR, 10.225; p <.001) and pulmonary fissure traversed by needle (HR, 10.776; p <.001). The C-statistics showed good predictive performance in the training and validation cohorts (0.792 and 0.832, respectively). Another nomogram for delayed pneumothorax was developed based on emphysema (HR, 2.952; p=.005), ablation zone encompassing pleura (HR, 4.915; p <.001) and pulmonary fissure traversed by needle (HR, 4.348; p =.015). The C-statistics showed good predictive performance in the training cohort, and it had efficacy for prediction in the validation cohort (0.719 and 0.689, respectively). The nomograms could effectively predict the risk of pneumothorax and delayed pneumothorax after MWA. [ABSTRACT FROM AUTHOR]

To develop an effective nomogram and artificial neural network (ANN) model for predicting pleural effusion after percutaneous microwave ablation (MWA) in lung malignancy (LM) patients. LM patients treated with MWA were randomly allocated to either the training cohort or the validation cohort (7:3). The predictors of pleural effusion identified by univariable and multivariable analyses in the training cohort were used to develop a nomogram and ANN model. The C-statistic was used to evaluate the predictive accuracy in both the training and validation cohorts. A total of 496 patients (training cohort: n = 357; validation cohort: n = 139) were enrolled in this study. The predictors selected into the nomogram for pleural effusion included the maximum power (hazard ratio [HR], 1.060; 95% confidence interval [CI], 1.022–1.100, p = 0.002), the number of pleural punctures (HR, 2.280; 95% CI, 1.103–4.722; p = 0.026) and the minimum distance from needle to pleura (HR, 0.840; 95% CI, 0.775–0.899; p < 0.001). The C-statistic showed good predictive performance in both cohorts, with a C-statistic of 0.866 (95% CI, 0.787–0.945) internally and 0.782 (95% CI, 0.644–0.920) externally (training cohort and validation cohort, respectively). The optimal cutoff value for the risk of pleural effusion was 0.16. Maximum power, number of pleural punctures and minimum distance from needle to pleura were predictors of pleural effusion after MWA in LM patients. The nomogram and ANN model could effectively predict the risk of pleural effusion after MWA. Patients showing a high risk (>0.16) on the nomogram should be monitored for pleural effusion. [ABSTRACT FROM AUTHOR]