Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

Journal: European radiology
PMID: 35616733

Abstract

OBJECTIVES: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently.

Authors

  • Jixin Meng
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Zixin Luo
  • Zhihui Chen
    Division of Cell and Developmental Biology, College of Life Science, University of Dundee, Dundee, UK.
  • Jie Zhou
    Departments of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences, Shanghai, China.
  • Zhao Chen
  • Baolan Lu
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Mengchen Zhang
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Yangdi Wang
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Chenglang Yuan
    School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
  • Xiaodi Shen
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Qinqin Huang
  • Zhuya Zhang
    Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Block A2, Lihu Campus of Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518000, People's Republic of China.
  • Ziyin Ye
    Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Qinghua Cao
    Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Zhiyang Zhou
    Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Yuancun Er Heng Road, NO.26, Guangzhou, 510655, People's Republic of China.
  • Yikai Xu
  • Ren Mao
    Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Minhu Chen
    Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Canhui Sun
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Ziping Li
    Guangdong Prov Acad Chinese Med Sci, Guangzhou Univ Chinese Med 510120, China. Electronic address: lzip_008@163.com.
  • Shi-Ting Feng
    Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Xiaochun Meng
    Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, 510655, China.
  • Bingsheng Huang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Xuehua Li
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China.

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