Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

Journal: Journal of translational medicine
PMID: 40301933

Abstract

BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma. However, not all patients with EGFR mutations respond to TKIs. This study aims to develop a deep learning radiological-pathological-clinical (DLRPC) model that integrates computed tomography (CT) images, hematoxylin and eosin (H&E)-stained aspiration biopsy samples, and clinical data to predict the response in EGFR-mutant lung adenocarcinoma patients undergoing TKIs treatment.

Authors

  • Taotao Yang
    Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
  • Xianqi Wang
    Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
  • Yuan Jin
  • Xiaohong Yao
    Shanxi Key Laboratory of Biomedical Metal Materials, College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, PR China.
  • Zhiyuan Sun
    Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China.
  • Pinzhen Chen
    Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
  • Suyi Zhou
    Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
  • Wentao Zhu
    Department of Computer Science, University of California, Irvine, CA, USA.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.

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