Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

Journal: Academic radiology
Published Date: May 22, 2025

Abstract

RATIONALE AND OBJECTIVES: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic information from whole-slide images (WSIs) to predict the epidermal growth factor receptor (EGFR) mutations of primary lung adenocarcinoma in patients with advanced-stage disease.

Authors

  • Junhui Xu
    Department of Sociology, School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
  • Bao Feng
    The Department of Radiology, Jiangmen Central Hospital/Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China.
  • XiangMeng Chen
    The Department of Radiology, Jiangmen Central Hospital/Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China.
  • Fei Wu
    Zhejiang University, 38 Zheda Road, Hangzhou 310058, Zhejiang, China.
  • Yu Liu
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
  • Zhaole Yu
    School of Automation, Guangxi University of Science and Technology, Liuzhou, Guangxi, China (J.X., Z.Y., X.D.).
  • SenLiang Lu
    School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, Guangxi Province, China.
  • Xiaobei Duan
    Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, PR China. Electronic address: 258573168@qq.com.
  • Xiaojuan Chen
    College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
  • KunWei Li
    The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-Sen University, NO.52 Meihuadong Street, Zhuhai, 519000, Guangdong Province, China.
  • Weibin Zhang
    Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China.
  • Xisheng Dai
    School of Automation, Guangxi University of Science and Technology, Liuzhou, Guangxi, China (J.X., Z.Y., X.D.). Electronic address: mathdxs@163.com.

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