Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer.

Journal: Clinical and translational gastroenterology
Published Date: Oct 1, 2019

Abstract

INTRODUCTION: Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC.

Authors

  • Qiong Li
    Department of Burns & Wound Care Centre, 2nd Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, 310000, Zhejiang Province, China. 2504131@zju.edu.cn.
  • Liang Qi
    Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
  • Qiu-Xia Feng
    Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Shu-Wen Sun
    Department of Radiology, First Affiliated Hospital With Nanjing Medical University, Nanjing, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Ying-Qian Ge
    CT Scientific Marketing, Siemens Healthcare, Shanghai, China.
  • Yu-Dong Zhang
    University of Leicester, Leicester, United Kingdom.
  • Xi-Sheng Liu
    Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.

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