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Magnifying Endoscopy with Narrow Band Imaging of Early Gastric Cancer: Correlation with Histopathology and Mucin Phenotype

机译:早期胃癌的窄带成像放大内镜:与组织病理学和粘蛋白表型的关系。

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Background/AimsMagnifying endoscopy with narrow band imaging (ME-NBI) is a useful modality for the detailed visualization of microsurface (MS) and microvascular (MV) structures in the gastrointestinal tract. This study aimed to determine whether the MS and MV patterns in ME-NBI differ according to the histologic type, invasion depth, and mucin phenotype of early gastric cancers (EGCs).MethodsThe MS and MV patterns of 160 lesions in 160 patients with EGC who underwent ME-NBI before endoscopic or surgical resection were prospectively collected and analyzed. EGCs were categorized as either differentiated or undifferentiated and as either mucosal or submucosal, and their mucin phenotypes were determined via immunohistochemistry of the tumor specimens.ResultsDifferentiated tumors mainly displayed an oval and/or tubular MS pattern and a fine network or loop MV pattern, whereas undifferentiated tumors mainly displayed an absent MS pattern and a corkscrew MV pattern. The destructive MS pattern was associated with submucosal invasion, and this association was more prominent in the differentiated tumors than in the undifferentiated tumors. MUC5AC expression was increased in lesions with either a papillary or absent MS pattern and a corkscrew MV pattern, whereas MUC6 expression was increased in lesions with a papillary MS pattern and a loop MV pattern. CD10 expression was more frequent in lesions with a fine network MV pattern.ConclusionsME-NBI can be useful for predicting the histopathology and mucin phenotype of EGCs.
机译:背景/目的带有窄带成像的放大内窥镜(ME-NBI)是用于详细显示胃肠道微表面(MS)和微血管(MV)结构的有用方式。本研究旨在根据早期胃癌(EGCs)的组织学类型,浸润深度和粘蛋白表型来确定ME-NBI中的MS和MV模式是否存在差异。方法160例EGC患者中160例病变的MS和MV模式在前瞻性地收集和分析内镜或手术切除前行ME-NBI。 EGCs分为分化型或未分化型,粘膜型或粘膜下型,并通过肿瘤标本的免疫组织化学确定其黏蛋白表型。未分化的肿瘤主要表现为无MS模式和开塞器MV模式。破坏性的MS模式与粘膜下浸润有关,这种联系在分化的肿瘤中比未分化的肿瘤中更为明显。在具有乳头或无MS模式和开瓶器MV模式的病变中,MUC5AC表达增加,而在具有乳头MS和环形MV模式的病变中,MUC6表达增加。 CD10的表达在具有良好网络MV模式的病变中更为常见。结论ME-NBI可用于预测EGC的组织病理学和粘蛋白表型。

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