Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI.

Journal: Clinical breast cancer
PMID: 32139272

Abstract

BACKGROUND: Axillary lymph node status is important for breast cancer staging and treatment planning as the majority of breast cancer metastasis spreads through the axillary lymph nodes. There is currently no reliable noninvasive imaging method to detect nodal metastasis associated with breast cancer.

Authors

  • Thomas Ren
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Renee Cattell
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY; Department of Biomedical Engineering.
  • Hongyi Duanmu
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY; Department of Computer Science, Stony Brook University, Stony Brook, NY.
  • Pauline Huang
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Haifang Li
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Rami Vanguri
    Department of Radiology, Columbia University Medical Center, New York, NY; Data Science Institute, Columbia University, New York, NY.
  • Michael Z Liu
    Department of Radiology, Columbia University Medical Center, New York, NY.
  • Sachin Jambawalikar
    Department of Radiology, Columbia University Medical Center, New York, NY.
  • Richard Ha
    Department of Radiology, Columbia University Medical Center, New York, NY.
  • Fusheng Wang
    Stony Brook University, Stony Brook, NY.
  • Jules Cohen
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Clifford Bernstein
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Lev Bangiyev
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Timothy Q Duong
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY. Electronic address: tim.duong@stonybrookmedicine.edu.

Keywords

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