An interpretable machine learning model assists in predicting induction chemotherapy response and survival for locoregionally advanced nasopharyngeal carcinoma using MRI: a multicenter study.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To develop and validate an interpretable and generalized machine learning model using MRI for the individualized prediction of induction chemotherapy (ICT) response and survival in locoregionally advanced nasopharyngeal carcinoma (LANPC).

Authors

  • Hai Liao
    Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China. 42442427@qq.com.
  • Yang Zhao
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Wei Pei
    Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Guangxi Clinical Research Center for Medical Imaging Construction, Guangxi Key Clinical Specialty (Medical Imaging Department), Dominant Cultivation Discipline of Guangxi Medical University Cancer Hospital (Medical Imaging Department), Nanning, China.
  • Xia Huang
    College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China.
  • Shiting Huang
    Department of Radiotherapy, Guangxi Medical University Cancer Hospital, Nanning, China.
  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Penghao Lai
    The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Zhejiang, P. R. China.
  • Weifeng Jin
    College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
  • Huayan Bao
    Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Guangxi Clinical Research Center for Medical Imaging Construction, Guangxi Key Clinical Specialty (Medical Imaging Department), Dominant Cultivation Discipline of Guangxi Medical University Cancer Hospital (Medical Imaging Department), Nanning, China.
  • Xueli Liang
    Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China.
  • Lei Xiao
    Intelligent Information Systems Institute, Department of Computer and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
  • Zhenyu Chen
    Academy of Computer Science and Technology, Anhui University, Hefei, China.
  • Shaolu Lu
    Department of Radiology, Wuzhou Red Cross Hospital, Wuzhou, China. lushaolu2008@163.com.
  • Danke Su
    Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China.
  • Bingfeng Lu
    Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China. 1435588951@qq.com.
  • Linghui Pan