Computed tomography enterography radiomics and machine learning for identification of Crohn's disease.

Journal: BMC medical imaging
PMID: 39506676

Abstract

BACKGROUND: Crohn's disease is a severe chronic and relapsing inflammatory bowel disease. Although contrast-enhanced computed tomography enterography is commonly used to evaluate crohn's disease, its imaging findings are often nonspecific and can overlap with other bowel diseases. Recent studies have explored the application of radiomics-based machine learning algorithms to aid in the diagnosis of medical images. This study aims to develop a non-invasive method for detecting bowel lesions associated with Crohn's disease using CT enterography radiomics and machine learning algorithms.

Authors

  • Qiao Shi
    Department of Radiology, Shenzhen Baoan Women's and Children's Hospital, #56, Yulv St., Baoan District, Shenzhen, Guangdong, 518102, People's Republic of China. docshi@126.com.
  • Yajing Hao
    Department of Radiology, Shenzhen Baoan Women's and Children's Hospital, #56, Yulv St., Baoan District, Shenzhen, Guangdong, 518102, People's Republic of China.
  • Huixian Liu
    Department of Radiology, Shenzhen Baoan Women's and Children's Hospital, #56, Yulv St., Baoan District, Shenzhen, Guangdong, 518102, People's Republic of China.
  • Xiaoling Liu
    Department of Endocrinology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
  • Weiqiang Yan
    Department of Medical Imaging, Peking University Shenzhen Hospital, Shenzhen, Guangdong, 518036, People's Republic of China.
  • Jun Mao
    Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
  • Bihong T Chen
    Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States. Electronic address: Bechen@coh.org.

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