Background and Aims

Magnifying narrow-band imaging (mag-NBI) and magnifying chromoendoscopy using crystal violet staining for pit pattern analysis (pit pattern) is highly accurate for diagnosing invasion depth of superficial colorectal neoplasms. However, NBI and pit pattern have not been compared.

Methods

We conducted an online survey of endoscopists interested in image-enhanced endoscopy. Still images using white light, mag-NBI, and pit pattern were collected from lesions diagnosed at the National Cancer Center Hospital (NCCH), Tokyo, Japan. Sixty endoscopists from outside NCCH who typically use magnifying endoscopy were recruited for this survey. We assessed the diagnostic accuracy using receiver operating characteristic (ROC) analysis based on a calculation of the area under the ROC curve.

Results

One hundred early colorectal neoplasms were selected for this survey. Histopathology revealed that, although 67 of the lesions had high-grade dysplasia or carcinoma with superficial submucosal (SM-s) invasion, the other 33 lesions had a carcinoma with deep submucosal invasion (SM-d). Comparing the area under the ROC curve from mag-NBI with that of pit pattern, the latter showed significantly higher diagnostic accuracy for depth invasion (0.83 [95% CI, 0.81-0.85] for mag-NBI, 0.88 [95% CI, 0.85-0.89] for pit pattern, P = .013).

Conclusions

Pit pattern should be the first choice for diagnosing invasion depth as the most reliable modality rather than mag-NBI.

中文翻译：

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放大彩色内窥镜检查与放大窄带成像对浅表性结直肠肿瘤的诊断性能比较：在线调查

背景和目标

使用结晶紫染色进行凹坑模式分析（凹坑模式）的放大窄带成像（mag-NBI）和放大的内窥镜检查对于诊断浅表性结直肠肿瘤的浸润深度非常准确。但是，尚未比较NBI和凹坑图案。

方法

我们对对图像增强内窥镜检查感兴趣的内镜医师进行了在线调查。使用白光，mag-NBI和凹坑图案的静止图像是从日本东京国立癌症中心医院（NCCH）诊断出的病变中收集的。本次研究共招募了60位来自NCCH外部的内镜医师，他们通常使用放大内窥镜检查。我们根据接收器工作特征（ROC）分析，基于ROC曲线下面积的计算，评估了诊断的准确性。

结果

本次调查选择了一百个早期大肠肿瘤。组织病理学显示，尽管其中67个病变为高度不典型增生或浅表黏膜下（SM-s）浸润癌，但其他33个病变为黏膜下浸润（SM-d）癌。将mag-NBI的ROC曲线下的面积与凹坑图案的ROC曲线下的面积进行比较，后者显示出对深度浸润的诊断准确度更高（mag-NBI的诊断准确度为0.83 [95％CI，0.81-0.85]，0.88 [95％CI，0.85 -0.89]（对于凹坑图案，P  = .013）。

结论

凹坑模式应该是诊断侵入深度的首选方法，而不是mag-NBI，它是最可靠的方法。