Computed Tomography-Based Radiomic Nomogram to Predict Occult Pleural Metastasis in Lung Cancer

Objectives: Preoperative identification of occult pleural metastasis (OPM) in lung cancer remains a crucial clinical challenge. This study aimed to develop and validate a predictive model integrating clinical information with chest CT radiomic features to preoperatively identify patients at risk of OPM. Methods: This study included 50 patients diagnosed with OPM during surgery as the positive training cohort and an equal number of non-metastatic patients as the negative control cohort. Using least absolute shrinkage and selection operator (LASSO) logistic regression, we identified key radiomic features and calculated radiomic scores. A predictive nomogram was developed by combining clinical characteristics and radiomic scores, which was subsequently validated using data from an additional 545 patients across three medical centers. Results: Univariate and multivariate logistic regression analyses identified carcinoembryonic antigen (CEA), neutrophil-to-lymphocyte ratio (NLR), clinical T stage, and tumor-pleural relationship as significant clinical predictors. The clinical model alone achieved an area under the curve (AUC) of 0.761. The optimal integrated model, combining radiomic scores from the volume of interest (VOI) with CEA and NLR, demonstrated improved predictive performance, with AUCs of 0.890 in the training cohort and 0.855 in the validation cohort. Conclusion: Radiomic features derived from CT scans show significant promise in identifying OPM in lung cancer. The nomogram developed in this study, which integrates CEA, NLR, and radiomic tumor area scores, enhances the precision of preoperative OPM prediction and provides a valuable tool for clinical decision-making.