首页|Magnifying endoscopy in upper gastroenterology for assessing lesions before completing endoscopic removal
Magnifying endoscopy in upper gastroenterology for assessing lesions before completing endoscopic removal
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NETL
NSTL
万方数据
维普
Any prognosis of gastrointestinal (GI) cancer is closely related to the stage of the disease at diagnosis.Endoscopic submucosal dissection (ESD) and en bloc endoscopic mucosal resection (EMR) have been performed as curative treatments for many early-stage GI lesions in recent years.The technologies have been widely accepted in many Asian countries because they are minimally invasive and supply thorough histopathologic evaluation of the specimens.However,before engaging in endoscopic therapy,an accurate diagnosis is a precondition to effecting the complete cure of the underlying malignancy or carcinoma in situ.For the past few years,many new types of endoscopic techniques,including magnifying endoscopy with narrow-band imaging (MENBI),have emerged in many countries because these methods provide a strong indication of early lesions and are very useful in determining treatment options before ESD or EMR.However,to date,there is no comparable classification equivalent to "Kudo's Pit Pattern Classification in the colon",for the upper GI,there is still no clear internationally accepted classification system of magnifying endoscopy.Therefore,in order to help unify some viewpoints,here we will review the defining optical imaging characteristics and the current representative classifications of microvascular and microsurface patterns in the upper GI tract under ME-NBI,describe the accurate relationship between them and the pathological diagnosis,and their clinical applications prior to ESD or en bloc EMR.We will also discuss assessing the differentiation and depth of invasion,defying the lateral spread of involvement and targeting biopsy in real time.
Magnifying endoscopy with narrow-band imagingUpper gastroenterologyAssessmentEndoscopic submucosal dissectionEndoscopic mucosal resection
NETL
NSTL
万方数据
维普
