Prognostic significance of the controlling nutritional status score in non-small cell lung cancer patients undergoing neoadjuvant therapy: Development of a predictive nomogram

Abstract OBJECTIVES To evaluate whether the Controlling Nutritional Status score measured before the start of neoadjuvant therapy is associated with overall survival and disease-free survival after lung resection for non-small-cell lung cancer, and to present a clinically usable prediction tool. METHODS This was a retrospective cohort study at a single tertiary centre. Eligible patients had stage II–III non-small-cell lung cancer, received at least one cycle of neoadjuvant systemic therapy, and subsequently underwent definitive pulmonary resection. Patients treated with neoadjuvant radiotherapy alone were excluded. Nutritional and inflammatory indices were obtained before neoadjuvant therapy. The primary end-points were overall survival and disease-free survival. Associations and model performance were assessed using prespecified multivariable analyses and standard validation procedures. RESULTS A total of 226 patients were included. Patients with a higher Controlling Nutritional Status score before neoadjuvant therapy had worse overall survival and disease-free survival after surgery (multivariable hazard ratio 3.759, 95% confidence interval 2.189–6.455, p＜0.001). Findings were consistent across treatment subgroups (chemotherapy and chemoimmunotherapy) and in sensitivity analyses using alternative thresholds. The prediction model showed acceptable discrimination and calibration at clinically relevant time points. CONCLUSIONS The controlling nutritional status score score is an independent and robust predictor of long-term outcomes in non-small cell lung cancer patients receiving neoadjuvant therapy. The developed nomogram is useful for postoperative risk stratification and individualized follow-up planning. Future studies should explore the integration of multiple nutritional and inflammatory markers into AI-based prognostic models to enhance predictive accuracy.