Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

Journal: Academic radiology
PMID:

Abstract

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs).

Authors

  • Guihan Lin
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China.
  • Weiyue Chen
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China.
  • Yingying Fan
    Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089; fanyingy@usc.edu fsun@usc.edu.
  • Yi Zhou
    Eye Center of Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Xia Li
    Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
  • Xin Hu
    Jiangxi Medical College, The First Affiliated Hospital, Nanchang University, Nanchang, China.
  • Xue Cheng
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China.
  • Mingzhen Chen
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
  • Chunli Kong
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China.
  • Minjiang Chen
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China.
  • Min Xu
    Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
  • Zhiyi Peng
    Department of Radiology, First Affiliated Hospital of Zhejiang University, Hangzhou, 310003, China.
  • Jiansong Ji
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China.