Prediction of EGFR Mutations in Lung Adenocarcinoma via CT Images: A Comparative Study of Intratumoral and Peritumoral Radiomics, Deep Learning, and Fusion Models.

Journal: Academic radiology
Published Date: May 5, 2025

Abstract

RATIONALE AND OBJECTIVES: This study aims to analyze the intratumoral and peritumoral characteristics of lung adenocarcinoma patients on the basis of chest CT images via radiomic and deep learning methods and to develop and validate a multimodel fusion strategy for predicting epidermal growth factor receptor (EGFR) mutation statuses.

Authors

  • Liyou Huang
    Department of Oncology, Affiliated Suqian Hospital of Xuzhou Medical University, Suqian 223800, PR China (L.H., L.X., L.W.).
  • Lu Xu
    School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Heifei Innovation Research Institute, Beihang University, Hefei, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; School of Biomedical Engineering, Anhui Medical University, Heifei, China.
  • Xun Wang
    College of Computer Science and Technology, China University of Petroleum, Dongying, China.
  • Guangbin Zhang
    Department of Radiology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou 215000, PR China (X.W., G.Z.).
  • Xiancong Gao
    Department of Radiology, Affiliated Suqian Hospital of Xuzhou Medical University, Suqian 223800, PR China (X.G., L.N.).
  • Lei Niu
    Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
  • Linchun Wen
    Department of Oncology, Affiliated Suqian Hospital of Xuzhou Medical University, Suqian 223800, PR China (L.H., L.X., L.W.). Electronic address: 760020240163@xzhmu.edu.cn.

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