Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Oct 15, 2021

Abstract

PURPOSE: Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment.

Authors

  • Chao An
    Department of Minimal invasive intervention, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Dongyang Li
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Sheng Li
    School of Data Science, University of Virginia, Charlottesville, VA, United States.
  • Wangzhong Li
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China.
  • Tong Tong
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Lizhi Liu
    Department of Anesthesiology, Division of Critical Care Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
  • Dongping Jiang
    Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
  • Linling Jiang
    Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
  • Guangying Ruan
    Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
  • Ning Hai
    Department of Ultrasound, Beijing Chao Yang Hospital, Capital Medical University, Beijing, 100010, China.
  • Yan Fu
    School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, PR China. Electronic address: fuyan@tju.edu.cn.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Shuiqing Zhuo
    Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China. zhuosq@sysucc.edu.cn.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

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