Application of convolutional neural networks for evaluating the depth of invasion of early gastric cancer based on endoscopic images

Background and Aim
Recently, artificial intelligence (AI) has been used in endoscopic examination and is expected to help in endoscopic diagnosis. We evaluated the feasibility of AI using convolutional neural network (CNN) systems for evaluating the depth of invasion of early gastric cancer (EGC), based on endoscopic images.

Methods
This study used a deep CNN model, ResNet152. From patients who underwent treatment for EGC at our hospital between January 2012 and December 2016, we selected 100 consecutive patients with mucosal (M) cancers and 100 consecutive patients with cancers invading the submucosa (SM cancers). A total of 3508 non-magnifying endoscopic images of EGCs, including white-light imaging, linked color imaging, blue laser imaging-bright, and indigo-carmine dye contrast imaging, were included in this study. A total of 2288 images from 132 patients served as the development dataset, and 1220 images from 68 patients served as the testing dataset. Invasion depth was evaluated for each image and lesion. The majority vote was applied to lesion-based evaluation.

Results
The sensitivity, specificity, and accuracy for diagnosing M cancer were 84.9% (95% confidence interval [CI] 82.3%–87.5%), 70.7% (95% CI 66.8%–74.6%), and 78.9% (95% CI 76.6%–81.2%), respectively, for image-based evaluation, and 85.3% (95% CI 73.4%–97.2%), 82.4% (95% CI 69.5%–95.2%), and 83.8% (95% CI 75.1%–92.6%), respectively, for lesion-based evaluation.

Conclusions
The application of AI using CNN to evaluate the depth of invasion of EGCs based on endoscopic images is feasible, and it is worth investing more effort to put this new technology into practical use.

This is the peer reviewed version of the following article: [Hamada, K., Kawahara, Y., Tanimoto, T., Ohto, A., Toda, A., Aida, T., Yamasaki, Y., Gotoda, T., Ogawa, T., Abe, M., Okanoue, S., Takei, K., Kikuchi, S., Kuroda, S., Fujiwara, T., and Okada, H. (2021) Application of convolutional neural networks for evaluating the depth of invasion of early gastric cancer based on endoscopic images, Journal of Gastroenterology and Hepatology], which has been published in final form at [https://doi.org/10.1111/jgh.15725]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages there of by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.