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Published on: February 16, 2024
Compound Endoscopic Morphological Features for Identifying Non-Pedunculated Lesions ≥20 mm with Intramucosal
João Pedro da Costa-Seixas1,2, María López-Cerón3,4, Anna Arnau5
1Gastrointestinal Oncology and Endoscopy Research Group, Digestive Diseases Department, Althaia Xarxa Assistencial Universitària de Manresa, 08243 Manresa, Spain.
Endoscopic features can predict intramucosal neoplasia in large non-pedunculated lesions. Non-ulcerated lesions, particularly LST-G and LST-NG flat elevated types, are suitable for piecemeal endoscopic mucosal resection (EMR).
Area of Science:
- Gastroenterology
- Endoscopic Imaging
- Oncology
Background:
- Piecemeal endoscopic mucosal resection (EMR) is limited by inaccurate histological assessment of resected specimens, particularly for submucosal invasion.
- Accurate pre-procedural classification of lesions is crucial for selecting appropriate EMR candidates.
Purpose of the Study:
- To classify non-pedunculated lesions ≥20 mm based on endoscopic morphology.
- To identify lesions with intramucosal neoplasia suitable for piecemeal EMR.
Main Methods:
- Post-hoc analysis of a prospective multicentre study involving 58 endoscopists.
- Conditional inference trees (CTREE) were used to analyze the association between intramucosal neoplasia and endoscopic characteristics.
- White light imaging and narrow-band imaging (NBI) were utilized.
Main Results:
- Intramucosal neoplasia was present in 89.3% of 542 analyzed lesions.
- Ulceration, pseudodepressed type, and sessile morphology significantly impacted the prediction accuracy of intramucosal neoplasia.
- Non-ulcerated LST-G and LST-NG flat elevated lesions showed a high probability (96.2%) of intramucosal neoplasia.
Conclusions:
- Non-ulcerated LST-G and LST-NG flat elevated lesions are common and highly likely to harbor intramucosal neoplasia, making them suitable for piecemeal EMR.
- For other lesion types, advanced techniques like magnification or endoscopic submucosal dissection may be necessary.
- Endoscopic morphological classification aids in selecting patients for piecemeal EMR, improving procedural outcomes.
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