Easily applicable predictive score for MPR based on parameters before neoadjuvant chemoimmunotherapy in operable NSCLC: a single-center, ambispective, observational study

Abstract

Background: Neoadjuvant chemoimmunotherapy (NACI) is promising for resectable non-small cell lung cancer (NSCLC), but predictive biomarkers are still lacking. We aimed to develop a model based on pretreatment parameters to predict major pathological response (MPR) for such an approach. Methods: We enrolled operable NSCLC treated with NACI between March 2020 and May 2023 and then collected baseline clinical-pathology data and routine laboratory examinations before treatment. The efficacy and safety data of this cohort was reported and variables were screened by Logistic and Lasso regression and nomogram was developed. In addition, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess its power. Finally, internal cross-validation and external validation was performed to assess the power of the model. Results: In total, 206 eligible patients were recruited in this study and 53.4% (110/206) patients achieved MPR. Using multivariate analysis, the predictive model was constructed by seven variables, prothrombin time (PT), neutrophil percentage (NEUT%), large platelet ratio (P-LCR), eosinophil percentage (EOS%), smoking, pathological type, and programmed death ligand-1 (PD-L1) expression finally. The model had good discrimination, with area under the ROC curve (AUC) of 0.775, 0.746 and 0.835 for all datasets, cross-validation and external validation, respectively. The calibration curves showed good consistency, and DCAs indicated its potential value in clinical practice. Conclusion: This real world study revealed favourable efficacy in operable NSCLC treated with NACI. The proposed model based on multiple clinically accessible parameters could effectively predict MPR probability and could be a powerful tool in personalized medication.