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

Journal: BMC medical informatics and decision making
Published Date: Jan 2, 2024

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 invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives.

Authors

  • Xiaoli Wu
    Burn Department of Maoming People's Hospital, Maoming Guangdong 525000, China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Shouliang Miao
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Guoquan Cao
    Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Huang Su
    Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, 325000, Zhejiang, China. suda0huan9@gmail.com.
  • Jie Pan
  • Yilun Xu
    Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.

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