Cha DI, Lee J, Jeong WK, et al. - This study sought to construct a prediction model with computed tomography (CT) images and to build a nomogram incorporating known clinicopathologic variables for individualized estimation of epithelial-to-mesenchymal transition (EMT) subtype gastric cancer. Researchers examined a total of 451 individuals who had undergone primary resection of gastric cancer (GC) and molecular subgroup analysis. Using a stepwise variable selection method , multivariable analysis was conducted to build a predictive model for EMT subtype GC. They ascertained an optimal cutoff value of total prognostic points of the nomogram for the prediction of EMT subtype. Researcults demonstrated that a predictive model using patient’s age, Lauren classification, and mural stratification on CT for EMT molecular subtype GC was made. This study’s findings demonstrate that a nomogram was built which would serve as a beneficial screening tool for an individualized estimate of EMT subtype.