Easily applicable predictive score for MPR based on parameters before neoadjuvant chemoimmunotherapy in operable...

Area of Science:

• Oncology
• Translational Medicine

Background:

• Neoadjuvant chemoimmunotherapy (NACI) shows promise for resectable non-small cell lung cancer (NSCLC).
• Predictive biomarkers for NACI response in NSCLC are currently lacking.
• Accurate prediction of treatment response is crucial for optimizing patient outcomes.

Purpose of the Study:

• To develop and validate a predictive model for major pathological response (MPR) in operable NSCLC patients undergoing NACI.
• To identify key pretreatment clinical-pathology parameters and laboratory values that predict MPR.
• To establish a tool for personalized treatment selection in NSCLC.

Main Methods:

• Retrospective analysis of 206 operable NSCLC patients treated with NACI.
• Collection of baseline clinical-pathology data and routine laboratory tests.
• Logistic and Lasso regression for variable selection, followed by nomogram construction.
• Validation using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Main Results:

• 53.4% of patients achieved MPR.
• A predictive model incorporating prothrombin time (PT), neutrophil percentage (NEUT%), large platelet ratio (P-LCR), eosinophil percentage (EOS%), smoking history, pathological type, and PD-L1 expression was developed.
• The model demonstrated good discrimination with AUCs of 0.775 (all data), 0.746 (cross-validation), and 0.835 (external validation).
• Calibration curves indicated good consistency, and DCA suggested clinical utility.

Conclusions:

• NACI demonstrates favorable efficacy in operable NSCLC.
• The developed predictive model, utilizing accessible parameters, can effectively predict MPR probability.
• This model serves as a valuable tool for guiding personalized medication strategies in NSCLC treatment.