Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date:

Abstract

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status.

Authors

  • Cheng Zhang
    College of Forestry, Jiangxi Agricultural University, Nanchang, Jiangxi Province, China.
  • Li-di Ma
    Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Xiao-Lan Zhang
    Shukun (Beijing) Technology Co, Ltd., Beijing, China.
  • Cai Lei
    Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Sha-Sha Yuan
    School of Information Science and Engineering, Qufu Normal University, Rizhao, China.
  • Jian-Peng Li
    Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China.
  • Zhi-Jun Geng
    Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Xin-Ming Li
    Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Xian-Yue Quan
    Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Chao Zheng
    School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515 People's Republic of China.
  • Ya-Yuan Geng
    Shukun (Beijing) Technology Co, Ltd., Beijing, China.
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Qiao-Li Zheng
    Department of Pathology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China.
  • Jing Hou
    Wuhan Institute for Food and Cosmetic Control, Wuhan 430014, China.
  • Shu-Yi Xie
    Department of Radiology, Guangzhou People's Eighth Hospital, Guangzhou, China.
  • Liang-He Lu
    Department of Hepatobiliary Surgery, Sun Yat-sen University Cancer Center, Guangzhou, 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.).