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Updated: Nov 2, 2025
Induction and Analysis of Epithelial to Mesenchymal Transition
Published on: August 27, 2013
Prediction of epithelial-to-mesenchymal transition molecular subtype using CT in gastric cancer
Dong Ik Cha1, Jeeyun Lee2, Woo Kyoung Jeong3
1Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.
Objectives:
To develop 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.
Methods:
Patients who underwent primary resection of gastric cancer (GC) and molecular subgroup analysis (n = 451) were reviewed. Multivariable analysis using a stepwise variable selection method was performed to build a predictive model for EMT subtype GC. A nomogram using the results of the multivariable analysis was constructed. An optimal cutoff value of total prognostic points of the nomogram for the prediction of EMT subtype was determined. The predictive model for the EMT subtype was internally validated by bootstrap resampling method.
Results:
There were 88 patients with EMT subtype and 363 patients with non-EMT subtype based on transcriptome analysis. The patient's age, Lauren classification, and mural stratification on CT were variables selected for the predictive model. The area under the curve (AUC) of the model was 0.865, and the validated AUC of the bootstrap sample was 0.860. The optimal cutoff value of total prognostic points for the prediction of EMT subtype was 94.622, with 90.9% sensitivity, 67.2% specificity, and 71.8% accuracy.
Conclusion:
A 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.
