Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study.

Journal: Academic radiology
PMID:

Abstract

RATIONALE AND OBJECTIVES: To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multiparametric magnetic resonance imaging (MRI) radiomic features for predicting treatment response to transarterial chemoembolization combined with molecular targeted therapy plus immunotherapy in unresectable hepatocellular carcinoma (HCC).

Authors

  • Wendi Kang
    Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Peiyun Tang
    Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130000, China.
  • Yingen Luo
    Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Qicai Lian
    Department of Interventional Radiology, the Affiliated Cancer Hospital of Guizhou Medical University, Guiyang 550000, China.
  • Xuan Zhou
    Clinical Trial Institution, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang 315100, China.
  • Jinrui Ren
    Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Tianhao Cong
    Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Lei Miao
    School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China. Electronic address: miaolei@seu.edu.cn.
  • Hang Li
    Beijing Academy of Quantum Information Sciences, Beijing 100193, China.
  • Xiaoyu Huang
    Key Laboratory of Organofluorine Chemistry and Laboratory of Polymer Materials, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China. Electronic address: xyhuang@sioc.ac.cn.
  • Aixin Ou
    Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhentao Yan
    Department of Interventional Radiology, The First Hospital of China Medical University, No.155 Nanjing Road, Heping District, Shenyang 110001, Liaoning, China.
  • Yingjie Di
    Department of Interventional Therapy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China.
  • Xiao Li
    Department of Inner Mongolia Clinical Medicine College, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
  • Feng Ye
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, National Center for Respiratory Medicine, Guangzhou, China.
  • Xiaoli Zhu
    Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an Road, Shanghai, 200032, China.
  • Zhengqiang Yang
    Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China. Electronic address: yangzq@cicams.ac.cn.