Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date: Jun 8, 2024

Abstract

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear.

Authors

  • Jialing Liu
    State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Xu Li
    Department of Critical Care Medicine, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
  • Gang Wang
    National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
  • Weixiong Zeng
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Hui Zeng
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Chanjuan Wen
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.
  • Weimin Xu
    Institute of Agricultural Products Processing, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, PR China.
  • Zilong He
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Genggeng Qin
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Weiguo Chen
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: chenweiguo1964@21cn.com.

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