Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements.

Authors

  • Ying Zhu
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Li-Li Chen
    Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China.
  • Ying-Wei Luo
    Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Hui-Yun Ma
    Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China.
  • Hao-Shuai Yang
    Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China.
  • Bao-Cong Liu
    Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China.
  • Lu-Jie Li
    Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China.
  • Wen-Biao Zhang
    Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China.
  • Xiang-Min Li
    Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China.
  • Chuan-Miao Xie
    From the Department of Radiation Oncology (L.L., G.Q.Z., J.Y.L., L.L.T., S.M.H., J.M., Y.S.) and Imaging Diagnosis and Interventional Center (C.M.X.), Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 651 Dongfeng Rd East, Guangzhou 510060, China; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR (Q.D., Y.M.J., P.A.H., H.C.); Imsight Medical Technology, Shenzhen, China (H.C.); Divisions of Radiation Oncology (J.T.S.W., M.L.K.C.) and Medical Sciences (M.L.K.C.), National Cancer Center Singapore, Singapore; Oncology Academic Programme, Duke-NUS Medical School, Singapore (M.L.K.C.); Department of Radiation Oncology, Jiangxi Cancer Hospital, Nanchang, China (Y.Q.T.); Department of Radiation Oncology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, China (W.L.C.); Department of Radiation Oncology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China (B.A.S.); Department of Radiation Oncology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China (F.L.); Department of Radiation Oncology, Zhejiang Provincial Cancer Hospital, Key Laboratory of Radiation Oncology of Zhejiang Province, Hangzhou, China (C.J.T.); and Department of Radiation Oncology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, China (N.J.).
  • Jian-Cheng Yang
    Dianei Technology, Shanghai, 200000, People's Republic of China. jekyll4168@sjtu.edu.cn.
  • De-Ling Wang
    Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China. wangdl@sysucc.org.cn.
  • Qiong Li
    Department of Burns & Wound Care Centre, 2nd Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, 310000, Zhejiang Province, China. 2504131@zju.edu.cn.