医学
金标准(测试)
弹性成像
恶性肿瘤
内镜超声
采样(信号处理)
细针穿刺
内镜超声检查
医学诊断
镊子
放射科
病理
活检
内窥镜检查
外科
超声波
滤波器(信号处理)
计算机科学
计算机视觉
作者
Osamu Goto,Mitsuru Kaise,Katsuhiko Iwakiri
出处
期刊:Gut and Liver
[Korean Association for the Study of the Liver]
日期:2022-05-15
卷期号:16 (3): 321-330
被引量:20
摘要
A diagnosis of subepithelial tumors (SETs) is sometimes difficult due to the existence of overlying mucosa on the lesions, which hampers optical diagnosis by conventional endoscopy and tissue sampling with standard biopsy forceps. Imaging modalities, by using computed tomography and endoscopic ultrasonography (EUS) are mandatory to noninvasively collect the target's information and to opt candidates for further evaluation. Particularly, EUS is an indispensable diagnostic modality for assessing the lesions precisely and evaluating the possibility of malignancy. The diagnostic ability of EUS appears increased by the combined use of contrast-enhancement or elastography. Histology is the gold standard for obtaining the final diagnosis. Tissue sampling requires special techniques to break the mucosal barrier. Although EUS-guided fine-needle aspiration (EUS-FNA) is commonly applied, mucosal cutting biopsy and mucosal incision-assisted biopsy are comparable methods to definitively obtain tissues from the exposed surface of lesions and seem more useful than EUS-FNA for small SETs. Recent advancements in artificial intelligence (AI) have a potential to drastically change the diagnostic strategy for SETs. Development and establishment of noninvasive methods including AI-assisted diagnosis are expected to provide an alternative to invasive, histological diagnosis.
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