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

Dong Ik ChaJeeyun LeeWoo Kyoung JeongSeung Tae KimJae-Hun KimJung Yong HongWon Ki KangKyoung-Mee KimSeon Woo KimDongil Choi

Published in: European radiology (2021)

• 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.

Keyphrases

computed tomography
image quality
dual energy
contrast enhanced
positron emission tomography
machine learning
magnetic resonance imaging
deep learning
signaling pathway
single molecule
pet ct