Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden During Neoadjuvant Chemotherapy in Breast Cancer.

Journal: Annals of surgery
Published Date:

Abstract

OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer.

Authors

  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Yu-Hong Huang
    Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
  • Teng Zhu
    Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
  • Yi-Min Zhang
    Clinical Research Center & Breast Disease Diagnosis and Treatment Center, Shantou Central Hospital, Shantou, China.
  • Xing-Xing Zheng
    Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
  • Ting-Feng Zhang
    Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
  • Ying-Yi Lin
    Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
  • Zhi-Yong Wu
    Clinical Research Center & Breast Disease Diagnosis and Treatment Center, Shantou Central Hospital, Shantou, China.
  • Zai-Yi Liu
    Department of Radiology, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
  • Ying Lin
    Clinical Laboratory, Dongzhimen Hospital Affiliated to BUCM, Beijing, China.
  • Guo-Lin Ye
    Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, China.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.