Prediction of epithelial-to-mesenchymal transition molecular subtype using CT in gastric cancer

ConclusionA predictive model using patient ’s age, Lauren classification, and mural stratification on CT for EMT molecular subtype GC was made. A nomogram was built which would serve as a useful screening tool for an individualized estimate of EMT subtype.Key Points•A predictive model for epithelial-to-mesenchymal transition (EMT) subtype incorporating patient ’s age, Lauren classification, and mural stratification on CT was built.•The predictive model had high diagnostic accuracy (area under the curve (AUC) = 0.865) and was validated (bootstrap AUC = 0.860).•Adding CT findings to clinicopathologic variables increases the accuracy of the predictive model than using only.
Source: European Radiology - Category: Radiology Source Type: research