Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.

Journal: Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Published Date: Jan 9, 2024

Abstract

BACKGROUND: Lung adenocarcinoma is a common cause of cancer-related deaths worldwide, and accurate EGFR genotyping is crucial for optimal treatment outcomes. Conventional methods for identifying the EGFR genotype have several limitations. Therefore, we proposed a deep learning model using non-invasive CT images to predict EGFR mutation status with robustness and generalizability.

Authors

  • Luoqi Weng
    Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
  • Yilun Xu
    Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
  • Yuhan Chen
    Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
  • Chengshui Chen
    Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Qinqing Qian
    Department of Respiratory Medicine, Shaoxing People's Hospital, Shaoxing, 312000, Zhejiang, China.
  • Jie Pan
  • Huang Su
    Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, 325000, Zhejiang, China. suda0huan9@gmail.com.

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