Research on Predicting the Efficacy of Immunotherapy for Non-small Cell Lung Cancer Based on CT Radiomics and Peripheral Blood Inflammatory Markers

IntroductionWe developed and validated predictive models based on CT radiomics and peripheral blood inflammatory markers to predict immunotherapy response in non-small cell lung cancer (NSCLC).MethodsThis retrospective study included 128 NSCLC patients receiving anti-PD-1/PD-L1 monotherapy as second-line therapy between January 2020 and January 2024. All eligible patients who met the inclusion criteria during the study period were consecutively included to minimize selection bias. Baseline non-contrast and contrast-enhanced chest CT acquired within 15 days before immunotherapy initiation was used for radiomics. Patients were randomly split into training and testing cohorts (7:3). Radiomics features were extracted and selected using LASSO with cross-validation followed by MRMR. A radiomics model, a clinical model (inflammatory markers), and a combined nomogram model were developed. Model performance was assessed by AUC and calibration; clinical utility was evaluated by decision curve analysis. Reporting followed TRIPOD+AI guidelines.ResultsThe combined model achieved the best performance (AUC 0.8802, 95% CI 0.8106-0.9497 in the training cohort; AUC 0.7329, 95% CI 0.5679-0.8979 in the testing cohort), outperforming the clinical model (AUC 0.6481 and 0.5325) and the radiomics model (AUC 0.7878 and 0.5621). DeLong tests for pairwise AUC comparisons yielded P values of 0.243 and 0.478, respectively.ConclusionsThe combined clinical-radiomics model may help identify NSCLC patients more likely to respond to immunotherapy. External validation is required before clinical implementation.