Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach

Abstract Background Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be...

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Bibliographic Details
Main Authors: Xiaoli Wu, Hao Wu, Shouliang Miao, Guoquan Cao, Huang Su, Jie Pan, Yilun Xu
Format: Article
Language:English
Published: BMC 2024-01-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Esophageal squamous cell carcinoma
Deep learning
Computed tomography
Online Access:https://doi.org/10.1186/s12911-023-02386-y

Internet

https://doi.org/10.1186/s12911-023-02386-y

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