Crohn's disease related strictures in cross‐sectional imaging: More than meets the eye?

Abstract

Strictures in Crohn's disease (CD) are a hallmark of long‐standing intestinal damage, brought about by inflammatory and non‐inflammatory pathways. Understanding the complex pathophysiology related to inflammatory infiltrates, extracellular matrix deposition, as well as muscular hyperplasia is crucial to produce high‐quality scoring indices for assessing CD strictures. In addition, cross‐sectional imaging modalities are the primary tool for diagnosis and follow‐up of strictures, especially with the initiation of anti‐fibrotic therapy clinical trials. This in turn requires such modalities to both diagnose strictures with high accuracy, as well as be able to delineate the impact of each histomorphologic component on the individual stricture. We discuss the current knowledge on cross‐sectional imaging modalities used for stricturing CD, with an emphasis on histomorphologic correlates, novel imaging parameters which may improve segregation between inflammatory, muscular, and fibrotic stricture components, as well as a future outlook on the role of artificial intelligence in this field of gastroenterology.

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 03, 2022
Source ID
10.1002/ueg2.12326

Entities

People

  • Cathy Lu
  • Florian Rieder
  • Ilyssa O. Gordon
  • Joseph Sleiman
  • Mark E. Baker
  • Namita S. Gandhi
  • Prathyush Chirra
  • Satish E Viswanath
  • Stenosis Therapy And Anti‐fibrotic Research (star) Consortium

Organizations

  • Case Western Reserve University
  • Cleveland Clinic
  • Congressionally Directed Medical Research Programs
  • National Cancer Institute
  • National Institute of Diabetes and Digestive and Kidney Diseases
  • University of Calgary
  • University of Pittsburgh

Tags

Fields of Study

  • Medicine

Readers

  • Immunology and Pathology
  • Thin Film Deposition Science.
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy