KRAS and gastric cancer: AI systems offer a cost-effective and time-efficient alternative for detecting gene mutations from histologic image (92). A multistep CNN networks based on the Inception-v3 architecture were trained to assess the mutational status of 5 genes (CDH1, ERBB2, KRAS, PIK3CA, and TP53) in GC, with CDH1 and PIK3CA exhibiting the highest accuracies of 0.847 and 0.834, respectively, for frozen WSIs and KRAS and CDH1 having the best accuracies of 0.894 and 0.820, respectively, for formalin-fixed paraffin-embedded tissue WSIs (93).