Combined laboratory and imaging indicators to construct risk models for predicting immunotherapy efficacy and...

This study aimed to investigate the correlations between short- and long-term efficacy of immune checkpoint inhibitors (ICIs) and pretreatment laboratory/imaging parameters in advanced non-small cell lung cancer (NSCLC), and to construct risk prediction models. We enrolled 137 NSCLC patients with stage IIIB-IV disease who completed 4 cycles of PD-1/PD-L1 inhibitor monotherapy or combination therapy. All participants underwent pretreatment laboratory assessments encompassing inflammatory markers, lymphocyte subsets, tumor biomarkers, coagulation profiles, and contrast-enhanced computed tomography (CE-CT) scans. The primary endpoints were objective response rate (ORR) and overall survival (OS), with progression-free survival (PFS) as the secondary endpoint. Univariate and multivariate logistic regression analyses were performed to identify significant predictors of short-term treatment response and develop an efficacy prediction model. For long-term outcomes, univariate and multivariate Cox proportional hazards regression analyses were conducted to establish a prognostic risk model. The final models were presented as nomograms and validated through receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). CD4+ T-cell count (P = .007), fibrinogen (FIB, P = .047), and mediastinal lymph node enlargement (P = .028) emerged as independent predictors of ORR. The prediction model demonstrated an area under the ROC curve (AUC) of 0.838, with bootstrap validation (1000 resamples) yielding a mean AUC of 0.867. Calibration analysis, DCA, and clinical impact curve (CIC) collectively confirmed the model's robust predictive performance. For OS, metastatic site (P = .007), neutrophil-to-lymphocyte ratio (NLR, P = .025), carbohydrate antigen 125 (CA125, P = .020), cytokeratin 19 fragment (CYFRA 21-1, P = .004), FIB (P < .001), and pleural effusion (P < .001) were identified as significant prognostic determinants. The model achieved AUC values of 0.858 and 0.860 for 1- and 2-year survival prediction, respectively. Calibration plots revealed excellent concordance between predicted and observed survival probabilities at both timepoints. Furthermore, DCA indicated superior net clinical benefit of the prognostic model compared to random chance models across threshold probability ranges. Comprehensive prediction models integrating clinical characteristics, laboratory biomarkers, and imaging parameters were developed for both short- and long-term efficacy evaluation of immunotherapy, offering clinically actionable guidance for personalizing treatment strategies in advanced NSCLC.