Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.
Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Republic of Korea.
J Med Internet Res. 2023 Oct 30;25:e50448. doi: 10.2196/50448.
Our research group previously established a deep-learning-based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis.
This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM.
A total of 11,868 endoscopic images were used for training and internal testing. The primary outcomes were lesion classification accuracy (6 classes: advanced gastric cancer, early gastric cancer, dysplasia, atrophy, IM, and normal) and atrophy and IM lesion segmentation rates for the segmentation model. The following tests were carried out to validate the performance of lesion classification accuracy: (1) external testing using 1282 images from another institution and (2) evaluation of the classification accuracy of atrophy and IM in real-world procedures in a prospective manner. To estimate the clinical utility, 2 experienced endoscopists were invited to perform a blind test with the same data set. A CDSS was constructed by combining the established 6-class lesion classification model and the preneoplastic lesion segmentation model with the previously established lesion detection model.
The overall lesion classification accuracy (95% CI) was 90.3% (89%-91.6%) in the internal test. For the performance validation, the CDSS achieved 85.3% (83.4%-97.2%) overall accuracy. The per-class external test accuracies for atrophy and IM were 95.3% (92.6%-98%) and 89.3% (85.4%-93.2%), respectively. CDSS-assisted endoscopy showed an accuracy of 92.1% (88.8%-95.4%) for atrophy and 95.5% (92%-99%) for IM in the real-world application of 522 consecutive screening endoscopies. There was no significant difference in the overall accuracy between the invited endoscopists and established CDSS in the prospective real-clinic evaluation (P=.23). The CDSS demonstrated a segmentation rate of 93.4% (95% CI 92.4%-94.4%) for atrophy or IM lesion segmentation in the internal testing.
The CDSS achieved high performance in terms of computer-aided diagnosis of all stages of gastric carcinogenesis and demonstrated real-world application potential.
我们的研究小组先前建立了一个基于深度学习的临床决策支持系统（CDSS），用于实时基于内镜的胃肿瘤检测和分类。然而，未考虑到前期病变，如萎缩和肠上皮化生（IM），也没有建立一个能够分类胃癌变所有阶段的模型。
本研究旨在建立和验证一个用于实时内镜的 CDSS，用于胃癌变的所有阶段，包括萎缩和 IM。
共使用了 11868 张内镜图像进行训练和内部测试。主要结果是病变分类准确率（6 类：进展期胃癌、早期胃癌、异型增生、萎缩、IM 和正常）和分割模型的萎缩和 IM 病变分割率。为了验证病变分类准确率，进行了以下测试：（1）使用来自另一个机构的 1282 张图像进行外部测试，（2）前瞻性评估真实世界程序中萎缩和 IM 的分类准确率。为了估计临床实用性，邀请了 2 名经验丰富的内镜医生使用相同的数据集进行盲法测试。通过将先前建立的 6 类病变分类模型和前期病变分割模型与已建立的病变检测模型相结合，构建了一个 CDSS。
内部测试中总体病变分类准确率（95%置信区间）为 90.3%（89%-91.6%）。对于性能验证，CDSS 实现了 85.3%（83.4%-97.2%）的总体准确率。外部测试中，萎缩和 IM 的分类准确率分别为 95.3%（92.6%-98%）和 89.3%（85.4%-93.2%）。在 522 例连续筛查内镜的真实世界应用中，CDSS 辅助内镜的萎缩准确率为 92.1%（88.8%-95.4%），IM 准确率为 95.5%（92%-99%）。在前瞻性真实临床评估中，受邀内镜医生与既定 CDSS 的总体准确率无显著差异（P=.23）。在内部测试中，CDSS 对萎缩或 IM 病变分割的分割率为 93.4%（95%置信区间 92.4%-94.4%）。
该 CDSS 在胃癌变所有阶段的计算机辅助诊断方面表现出了较高的性能，并展示了实际应用的潜力。
