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

Objective: The 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 that integrates 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 nonmetastatic 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 with data from an additional 545 patients across three medical centers.

Results: Univariate and multivariate logistic regression analyses revealed that carcinoembryonic antigen (CEA), the neutrophil-to-lymphocyte ratio (NLR), the clinical T stage, and the tumor-pleural relationship were significant clinical predictors. The clinical model alone achieved an area under the curve (AUC) of 0.761. The optimal integrated model, which combined radiomic scores from the volume of interest (VOI) with the CEA and NLR, demonstrated an improved predictive performance, with AUCs of 0.890 in the training cohort and 0.855 in the validation cohort.

Conclusions: Radiomic features derived from CT scans show significant promise in identifying patients with lung cancer at risk of OPM. The nomogram developed in this study, which integrates CEA, the NLR, and radiomic tumor area scores, enhances the precision of preoperative OPM prediction and provides a valuable tool for clinical decision-making.