Machine Learning Models Based on CT Enterography for Differentiating Between Ulcerative Colitis and Colonic Crohn's Disease Using Intestinal Wall, Mesenteric Fat, and Visceral Fat Features.

Journal: Academic radiology
Published Date:

Abstract

PURPOSE: This study aimed to develop radiomic-based machine learning models using computed tomography enterography (CTE) features derived from the intestinal wall, mesenteric fat, and visceral fat to differentiate between ulcerative colitis (UC) and colonic Crohn's disease (CD).

Authors

  • Xia Wang
    Department of Neurology, The Sixth People's Hospital of Huizhou City, Huizhou, China.
  • Xingwei Wang
    Roche Tissue Diagnostics, Imaging and Algorithms, Digital Pathology, Santa Clara, California.
  • Jie Lei
    State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China. Electronic address: jielei@mail.xidian.edu.cn.
  • Chang Rong
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China.
  • Xiaomin Zheng
    Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, No.2 Fuxue Lane, Wenzhou, 325000, People's Republic of China.
  • Shuai Li
    School of Molecular Biosciences, Center for Reproductive Biology, College of Veterinary Medicine, Washington State University.
  • Yankun Gao
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
  • Xingwang Wu
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Keywords

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