Artificial intelligence for evaluating the risk of gastric cancer: reliable detection and scoring of intestinal metaplasia with deep learning algorithms.

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Abstract

Gastric cancer (GC) is associated with chronic gastritis. To evaluate the risk, the Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) system was constructed and showed a higher GC risk in stage III or IV patients, determined by the degree of intestinal metaplasia (IM). While the OLGIM system is useful, evaluating the degree of IM requires substantial experience to produce precise scoring. Whole-slide imaging is becoming routine, but most artificial intelligence (AI) systems in pathology are focused on neoplastic lesions.Hematoxylin and eosin-stained slides were scanned. Images were divided into each gastric biopsy tissue and labelled with an IM score. IM was scored as follows: 0 (no IM), 1 (mild IM), 2 (moderate IM), and 3 (severe IM). Overall, 5,753 images were prepared. A deep convolutional neural network (DCNN) model, ResNet50, was used for classification.ResNet50 classified images with and without IM with a sensitivity of 97.7% and specificity of 94.6%. IM scores 2 and 3, those are involved as criteria of stage III or IV in the OLGIM system were classified by ResNet50 in 18 %. The respective sensitivity and specificity values of classifying IM between scores 0, 1, and 2, 3 were 98.5% and 94.9%. The IM scores classified by pathologists and the AI system were different in only 438 (7.6%) of all images, and we found that ResNet50 tended to miss small foci of IM, while it successfully identified minimal IM areas that pathologists missed during the review.Our findings suggested that this AI system would contribute to evaluating the risk of gastric cancer accuracy, reliability, and repeatability with worldwide standardization.Copyright © 2023 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.