Clinical multi-dimensional prognostic nomogram for predicting the efficacy of immunotherapy in NSCLC

Abstract The advent of immunotherapy has greatly improved the prognosis of non-small cell lung (NSCLC) patients. However, given its low response rate and high cost of treatment, the search for valuable predictive markers of treatment efficacy is necessary. Considering the complexity and heterogeneity of the tumour and tumour microenvironment, the construction of a multi-dimensional prediction model is necessary. Therefore, we aimed to integrate clinical parameters, radiomic features, and immune signature data from NSCLC patients receiving immunotherapy to construct a multi-dimensional prediction model to better predict the efficacy of immunotherapy. The current study enrolled 137 NSCLC patients who received immunotherapy. We collected baseline clinical information, CT images, and tumour tissue specimens. Using 3D-Slicer software, radiomic features were extracted from patient CT images, and tumor tissue samples obtained before immunotherapy were subjected to immunohistochemical staining. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to downscale the data, and the radiomic features and immune signatures associated with the prognosis of immunotherapy patients were identified. The modified lung immune predictive index (mLIPI), radiomics score (Radioscore), immune score and multi-dimensional model nomogram were constructed. The C-index and area under the curve (AUC) were applied to evaluate the predictive efficacy of the models. Three radiomic features and three immune signatures that could predict the efficacy of immunotherapy were eventually screened. Multivariate analysis showed that the mLIPI, Radioscore, and immune score were independent predictive factors for PFS and OS (P