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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