Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach.

Journal: Magnetic resonance imaging
PMID: 39880177

Abstract

BACKGROUND: Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectiveness of integrating MRI radiomic features with clinical data using a deep learning approach to predict vascular invasion in breast cancer patients.

Authors

  • Guihai Pan
    Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China.
  • Zejun Pan
    Zhejiang Key Laboratory of Large-Scale Integrated Circuit Design, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Wubiao Chen
    Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China.
  • Yongjun Wu
    Department of Health Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, China. wuyongjun@zzu.edu.cn.
  • Xiaoqing Di
    Department of Pathology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China.
  • Fei Zhou
    College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
  • Yuting Liao

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