Unveiling Lymph Node Metastasis (LNM) in pleural-attached lung mucinous adenocarcinoma: A Predictive Model Using Pleural Contact Parameters

Objective: This study seeks to evaluate the prognostic significance of pleural-attached parameters and computed tomography (CT) imaging characteristics in predicting lymph node metastasis (LNM) in cases of pleural-attached invasive mucinous adenocarcinoma (IMA). Methodology: A retrospective analysis was conducted on a cohort of 276 IMA patients from three public tertiary hospitals in China, covering the period from January 2017 to December 2022. A comparative analysis was performed on pleural-attached parameters and CT imaging characteristics between different patient groups. Based on variables that showed statistical significance, nine machine learning models were developed, and the model with the highest area under the curve (AUC) value was identified as the optimal model. Patients were randomly allocated into training and testing groups in a 7:3 ratio. The 5-fold cross-validation technique was employed to evaluate the receiver operating characteristic (ROC) curve AUC value of the most effective machine learning model. Results: Both univariate and multivariate logistic regression analyses identified pleural contact length, pleural contact surface area (CSA), the skirt-like sign, cavity sign, angiogram sign, and lymph node enlargement as significant predictive factors for lymph node metastasis (LNM) in pleural-attached invasive mucinous adenocarcinoma (IMA). The logistic regression model demonstrated superior performance, achieving ROC-AUC values of 0.856 and 0.803 in the training and test groups, respectively. Further analysis of the model in patients without lymph node enlargement indicated that it maintained superior performance, with AUC values of 0.880 in the entire training cohort and 0.883 in the test cohort, and accuracy rates of 0.826 and 0.838, respectively. Conclusion: The logistic regression model, which incorporates pleural contact parameters and imaging characteristics, demonstrated substantial diagnostic value for assessing LNM in pleural-attached IMA, particularly in patients without lymph node enlargement. It exhibited excellent diagnostic efficacy and provides a non-invasive evaluation method for clinical practice.