Interpretable deep learning insights: Unveiling the role of 1 Gy volume on lymphopenia after radiotherapy in breast cancer.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date:

Abstract

BACKGROUND: Lymphopenia is known for its significance on poor survivals in breast cancer patients. Considering full dosimetric data, this study aimed to develop and validate predictive models for lymphopenia after radiotherapy (RT) in breast cancer.

Authors

  • Fang Chen
  • Ping Zhou
  • Ge Ren
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
  • Eric K W Lee
    Department of Clinical Oncology, Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
  • Qin Liu
    School of Public Health and Management, Research Center for Medicine and Social Development, Innovation Center for Social risk Governance in Health, Chongqing Medical University, Chongqing 400016, China.
  • Yuanyuan Shen
    Knowledge Engineering and Discovery Research Institute (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand.
  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Aya El Helali
    Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
  • Jian-Yue Jin
    University Hospitals/Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio.
  • Pingfu Fu
    Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
  • Wei Dai
    Department of Intensive Care Unit, The First Affiliated Hospital of Jiangxi Medical College, Shangrao, Jiangxi, China.
  • Anne W M Lee
    Department of Clinical Oncology, Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
  • Hao Yu
    Shanghai Key Lab of Trustworthy Computing, East China Normal University, Shanghai, China.
  • Feng-Ming Spring Kong
    Department of Clinical Oncology, Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China; Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China. Electronic address: kong0001@hku.hk.