CT radiomics and morphological characteristics for predicting PD-L1 expression on tumor cells and tumor infiltrating lymphocytes in gastric cancer

Purpose To explore CT radiomics and morphological characteristics for predicting programmed cell death ligand 1 on tumor cells (PD-L1) and tumor infiltrating lymphocytes (PD-L1-TILs) status in gastric cancer (GC).Methods From March 2019 to October 2019, 101 patients identified with GC who underwent surgery at our hospital were enrolled in this study retrospectively. Radiomic features were extracted from regions of interest manually drawn on venous CT images. Besides, 4 morphological characteristics were evaluated. The signatures based on radiomics and morphological characteristics were built using multiple classifiers (Support Vector Machine [SVM], Naive Bayes [NB], Decision Trees, and Random Forest). Receiver operating characteristic (ROC) curve was performed to assess diagnostic efficiency.Results The adjacent adipose tissue (p=0.009) and numerous radiomic features (all p<0.05) differed significantly between GCs with different PD-L1 status. Six radiomic features showed significant differences between different PD-L1-TILs status (all p<0.05). The highest areas under the ROC curves (AUCs) of signatures generated by classifiers were 0.807 (SVM) and 0.729 (NB) for the prediction of PD-L1 and PD-L1-TILs status, respectively.Conclusion It was promising to predict PD-L1 status in GCs noninvasively using CT radiomics combined with morphological characteristics. It might help to improve clinical decision making with regard to immunotherapy. However, the prediction for PD-L1-TILs needs to be explored further.