CT imaging-based nomogram for predicting pleural invasion in non-small cell lung cancer.

OBJECTIVE: To identify risk factors for visceral pleural invasion (VPI) in non-small cell lung cancer (NSCLC) using preoperative CT characteristics and develop a nomogram for VPI prediction. METHODS: We collected clinical data from 282 NSCLC patients (July 2020-June 2022) who underwent CT scans. Patients were categorized into VPI (n=70) and non-VPI (n=212) groups. Demographic, pathologic, and CT characteristics were analyzed. Logistic regression was applied to identify VPI-related determinants, and a nomogram was developed. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. RESULTS: No significant differences were observed between the groups for gender, age, BMI, disease duration, KPS score, smoking/alcohol history, tumor location, pathologic type, or CT features (all P>0.05). The VPI group had higher proportions of moderately/poorly differentiated tumors, advanced N-stage, M1-stage, larger tumor size, pleural retraction, pleura-adherence, and irregular morphology (all P<0.05). Multivariate analysis identified significant predictors: moderate/poor differentiation (OR=2.41), N2-N3 stage (OR=3.17), M1 stage (OR=2.89), pleural retraction (OR=4.02), pleura adherence (OR=3.55), irregular morphology (OR=3.21), and larger tumor diameter (OR=1.98) (all P<0.05). The model exhibited AUCs of 0.933 (training) and 0.959 (validation). Bootstrap validation (B=1000) indicated good calibration. The VPI group had a lower 3-year survival rate (69.57%) compared to the non-VPI group (80.66%) (P<0.05). CONCLUSION: VPI in NSCLC is associated with worse prognosis. The CT features, including pleural indentation and irregular morphology, may be used to predict VPI. The nomogram developed will be a valuable tool for clinical decision-making and improving patient outcomes.