Descriptive statistics were calculated and presented by groups, using median and interquartile range (IQR) for continuous variables and frequency and percentage for categorical variables. Continuous variables were compared between groups using Wilcoxon rank sum test and for categorical variables we used Chi-squared test or Fisher’s exact test if any cell count was 5 or less. Given the nonrandom treatment assignment and to minimize selection bias, we matched 1:1 uniportal patients and multiportal patients using propensity score matching methods. We performed a multivariate logistic model to calculate propensity scores for each patient accounting for the following covariates: age, sex, tumor location (upper or lower), Charlson Comorbidity Index, previous lung surgery, clinical tumor size (not pathologic), smoking status, BMI, and FEV1%. Standardized mean differences were assessed before and after matching. No differences were found after matching, when comparing demographics and baseline characteristics between the matched groups. We performed a post-match balance assessment and generated 2 groups with 156 patients in each cohort. Data was analyzed before and after propensity score matching and used SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA). We considered P values less than 0.05 as statistically significant.