An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.

Journal: BMC cancer
Published Date:

Abstract

BACKGROUND: The accurate early-stage diagnosis of gallbladder cancer (GBC) is regarded as one of the major challenges in the field of oncology. However, few studies have focused on the comprehensive classification of GBC based on multiple modalities. This study aims to develop a comprehensive diagnostic framework for GBC based on both imaging and non-imaging medical data.

Authors

  • Huiyu Zhao
    The State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Chuang Miao
    State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Yidi Zhu
    Department of General Surgery, Affiliated to Shanghai, Xinhua Hospital, Jiao Tong University School of Medicine, Shanghai, 200092, China.
  • Yijun Shu
    Department of General Surgery, Affiliated to Shanghai, Xinhua Hospital, Jiao Tong University School of Medicine, Shanghai, 200092, China.
  • Xiangsong Wu
    Department of General Surgery, Affiliated to Shanghai, Xinhua Hospital, Jiao Tong University School of Medicine, Shanghai, 200092, China.
  • Ziming Yin
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Xiao Deng
    The State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Wei Gong
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
  • Ziyi Yang
    Microsoft Research, Redmond, WA, USA.
  • Weiwen Zou