Computer-aided diagnosis for characterization of colorectal lesions: comprehensive software that includes differentiation of serrated lesions.,Gastrointestinal Endoscopy

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Computer-aided diagnosis for characterization of colorectal lesions: comprehensive software that includes differentiation of serrated lesions.
Gastrointestinal Endoscopy ( IF 7.7 ) Pub Date : 2020-03-04 , DOI: 10.1016/j.gie.2020.02.042
Leonardo Zorron Cheng Tao Pu ₁ , Gabriel Maicas ₂ , Yu Tian ₃ , Takeshi Yamamura ₄ , Masanao Nakamura ₅ , Hiroto Suzuki ₄ , Gurfarmaan Singh ₆ , Khizar Rana ₆ , Yoshiki Hirooka ₇ , Alastair D Burt ₆ , Mitsuhiro Fujishiro ₅ , Gustavo Carneiro ₂ , Rajvinder Singh ₈

Affiliation

Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia; Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia.
Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia; South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
Department of Endoscopy, Nagoya University Hospital, Nagoya, Aichi, Japan.
Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.
Department of Liver, Biliary Tract and Pancreas Diseases, Fujita Health University, Toyoake, Aichi, Japan.
Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia; Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, Adelaide, South Australia, Australia.

Background and Aims

Endoscopy guidelines recommend adhering to policies such as resect and discard only if the optical biopsy is accurate. However, accuracy in predicting histology can vary greatly. Computer-aided diagnosis (CAD) for characterization of colorectal lesions may help with this issue. In this study, CAD software developed at the University of Adelaide (Australia) that includes serrated polyp differentiation was validated with Japanese images on narrow-band imaging (NBI) and blue-laser imaging (BLI).

Methods

CAD software developed using machine learning and densely connected convolutional neural networks was modeled with NBI colorectal lesion images (Olympus 190 series - Australia) and validated for NBI (Olympus 290 series) and BLI (Fujifilm 700 series) with Japanese datasets. All images were correlated with histology according to the modified Sano classification. The CAD software was trained with Australian NBI images and tested with separate sets of images from Australia (NBI) and Japan (NBI and BLI).

Results

An Australian dataset of 1235 polyp images was used as training, testing, and internal validation sets. A Japanese dataset of 20 polyp images on NBI and 49 polyp images on BLI was used as external validation sets. The CAD software had a mean area under the curve (AUC) of 94.3% for the internal set and 84.5% and 90.3% for the external sets (NBI and BLI, respectively).

Conclusions

The CAD achieved AUCs comparable with experts and similar results with NBI and BLI. Accurate CAD prediction was achievable, even when the predicted endoscopy imaging technology was not part of the training set.

中文翻译：

大肠病变特征的计算机辅助诊断：包括锯齿状病变鉴别的综合软件。

背景和目标

内窥镜检查指南建议仅在光学活检准确的情况下才遵守切除和丢弃等政策。但是，预测组织学的准确性可能相差很大。用于表征结肠直肠病变的计算机辅助诊断（CAD）可能有助于解决此问题。在这项研究中，澳大利亚的阿德莱德大学开发的包括锯齿状息肉分化的CAD软件已通过日本在窄带成像（NBI）和蓝激光成像（BLI）上的图像进行了验证。

方法

使用NBI大肠病变图像（奥林巴斯190系列-澳大利亚）对使用机器学习和紧密连接的卷积神经网络开发的CAD软件进行建模，并使用日本数据集对NBI（奥林巴斯290系列）和BLI（富士700系列）进行了验证。根据改良的Sano分类，所有图像均与组织学相关。CAD软件接受了澳大利亚NBI图像的培训，并分别接受了来自澳大利亚（NBI）和日本（NBI和BLI）的图像集的测试。