Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

PURPOSE: This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.

Authors

  • Jiabin Yu
    Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Beijing, 100048, China. 94607969@qq.com.
  • Qi Liu
    National Institute of Traditional Chinese Medicine Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China.
  • Chenjie Xu
    College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, 310018, China.
  • Qinli Zhou
    College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, 310018, China.
  • Jiajun Xu
  • Lingying Zhu
    Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Branch of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, Zhejiang, China. whitemouse811@hotmail.com.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Yahan Zhou
    Wenling Medical Big Data and Artificial Intelligence Research Institute, Taizhou, China.
  • Binggang Xiao
    College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, 310018, China.
  • Lin Zheng
    Department of Minimally Invasive Intervention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, ZhengZhou, 450008, China.
  • Xiaofeng Zhou
    College of Education, Zhejiang Normal University, Jinhua, Zhejiang Province, China.
  • Fengming Zhang
    Taizhou Campus, Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, Zhejiang, 317502, China.
  • Yuhang Ye
    Taizhou Campus, Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, Zhejiang, 317502, China.
  • Hongmei Mi
    College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, 310018, China.
  • Dongping Zhang
    School of Automation, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: zdpzdp1234@163.com.
  • Li Yang
    Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Zhiwei Wu
    Center for Public Health Research, Medical School of Nanjing University, Nanjing, People's Republic of China.
  • Jiayi Wang
    Department of Statistics, Texas A&M University.
  • Ming Chen
    Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China.
  • Zhirui Zhou
    Radiation Oncology Center, Shanghai Medical College, Huashan Hospital, Fudan University, No.12 Wulumuqi Middle Road, Shanghai, 201107, China.
  • Haoyang Wang
    School of Life Sciences, Central South University, Changsha 410013, China.
  • Vicky Y Wang
    Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Department of Radiology Imaging, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), No. 50 Zhenxin Road, Xinhe Town, Wenling, Zhejiang, 317502, China. wangyang@waiim.org.cn.
  • Enyu Wang
    Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Branch of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, Zhejiang, China.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.