For instance, AI–based pathology has been utilized to predict survival benefit from adjuvant therapy in early-stage NSCLC [595], as well as to identify and segment whole-slide images (WSIs) of lung tissue into tumor and non-tumor regions [582, 596]. Additionally, an AI-powered multimodal approach that integrates H&E staining and CT imaging has demonstrated complementary and synergistic effects in predicting clinical outcomes of ICI therapy in advanced NSCLC, including in the PD-L1 high-expression subgroup [597, 598]. This evidence concerns the gene CD274 and neoplasm.