A standard terminology has been developed for small bowel (SB) video capsule endoscopy (SBCE) as Capsule Endoscopy Structured Terminology (CEST). It followed the model of the Minimal Standard Terminology (MST) created under the sponsorship of the Organization Mondiale d’Endoscopie Digestive for upper, lower, and hepato-biliary endoscopy. In parallel, MST 3.0 version was extended to also include SB lesions and the enteroscopy procedures. Both terminologies follow the principle of describing elementary lesions using headings as normal, mucosa, lumen, and flat, excavated, or elevated lesions. Within these headings, specific terms are used to classify lesions together with further attributes and their values as, e.g., size, shape, number, and distribution. Often, a combination of terms and attributes is necessary to describe a finding. CEST has been validated, and is included in some softwares and in training artificial intelligence by deep learning. The structure of CEST and corresponding images is provided in this chapter. In addition, experts used Delphi process to develop a consensus with iterative approximations on nomenclature and description of vascular and inflammatory findings in the SB, as well as on villous atrophy. Examples are included in this chapter.

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Terminology

  • Romain Leenhardt,
  • Xavier Dray,
  • Luca Elli,
  • Martin Keuchel,
  • Peter Baltes

摘要

A standard terminology has been developed for small bowel (SB) video capsule endoscopy (SBCE) as Capsule Endoscopy Structured Terminology (CEST). It followed the model of the Minimal Standard Terminology (MST) created under the sponsorship of the Organization Mondiale d’Endoscopie Digestive for upper, lower, and hepato-biliary endoscopy. In parallel, MST 3.0 version was extended to also include SB lesions and the enteroscopy procedures. Both terminologies follow the principle of describing elementary lesions using headings as normal, mucosa, lumen, and flat, excavated, or elevated lesions. Within these headings, specific terms are used to classify lesions together with further attributes and their values as, e.g., size, shape, number, and distribution. Often, a combination of terms and attributes is necessary to describe a finding. CEST has been validated, and is included in some softwares and in training artificial intelligence by deep learning. The structure of CEST and corresponding images is provided in this chapter. In addition, experts used Delphi process to develop a consensus with iterative approximations on nomenclature and description of vascular and inflammatory findings in the SB, as well as on villous atrophy. Examples are included in this chapter.