Multiparameter spectral CT-based radiomics in predicting the expression of programmed death ligand 1 in non-small-cell lung cancer

To explore the value of radiomics for predicting the expression of programmed death ligand 1 (PD-L1) in non-small-cell lung cancer (NSCLC) based on multiparameter spectral computed tomography (CT) images. A total of 220 patients with NSCLC were enrolled retrospectively and divided into the training (n=176) and testing (n=44) cohorts. The radiomics features were extracted from the conventional CT images, mono-energy 40 keV images, iodine density (ID) maps, Z-effective maps, and electron density maps. The logistic regression (LR) and support vector machine (SVM) algorithms were employed to build models based on radiomics signatures. The prediction abilities were qualified by the area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve. Internal validation was performed on the independent testing dataset. The combined model for PD-L1 ≥1%, which consisted of the radiomics score (rad-score; p