Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists.

Journal: European journal of radiology
Published Date: Jun 9, 2024

Abstract

PURPOSE: To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy of this fusion model against the assessments made by experienced radiologists.

Authors

  • Tao Yu
    Department of Smart Experience Design Kookmin University, Seoul 02707, Republic of Korea.
  • Renqiang Yu
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
  • Mengqi Liu
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Xingyu Wang
    1 Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, P. R. China.
  • Jichuan Zhang
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, China.
  • Yineng Zheng
    Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
  • Fajin Lv
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

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