Radiomics nomogram for predicting chemo-immunotherapy efficiency in advanced non-small cell lung cancer

Abstract This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78–0.92), 0.76 (95% CI 0.68–0.84), and 0.89 (95% CI 0.84–0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86–1.00), 0.79 (95% CI 0.68–0.91), and 0.96 (95% CI 0.90–1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72–0.96), 0.75 (95% CI 0.62–0.87), and 0.86 (95% CI 0.75–0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.