Abstract：
BACKGROUND: Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to assess comprehensive pathologic characteristics of EGC using white-light endoscopic images and videos.
METHODS: To train the model, we retrospectively collected 4,336 images and prospectively included 153 videos from patients with EGC who underwent endoscopic or surgical resection. The performance of the model was tested and compared to that of 16 endoscopists (nine experts and seven novices) using a mutually exclusive set of 260 images and 10 videos. Finally, we conducted external validation using 436 images and 89 videos from another institution.
RESULTS: After training, the model achieved predictive accuracies of 89.7% for undifferentiated histology, 88.0% for submucosal invasion, 87.9% for lymphovascular invasion (LVI), and 92.7% for lymph node metastasis (LNM), using endoscopic videos. The area under the curve values of the model were 0.992 for undifferentiated histology, 0.902 for submucosal invasion, 0.706 for LVI, and 0.680 for LNM in the test. In addition, the model showed significantly higher accuracy than the experts in predicting undifferentiated histology (92.7% vs. 71.6%), submucosal invasion (87.3% vs. 72.6%), and LNM (87.7% vs. 72.3%). The external validation showed accuracies of 75.6% and 71.9% for undifferentiated histology and submucosal invasion, respectively.
CONCLUSIONS: AI may assist endoscopists with high predictive performance for differentiation status and invasion depth of EGC. Further research is needed to improve the detection of LVI and LNM.
METHODS: To train the model, we retrospectively collected 4,336 images and prospectively included 153 videos from patients with EGC who underwent endoscopic or surgical resection. The performance of the model was tested and compared to that of 16 endoscopists (nine experts and seven novices) using a mutually exclusive set of 260 images and 10 videos. Finally, we conducted external validation using 436 images and 89 videos from another institution.
RESULTS: After training, the model achieved predictive accuracies of 89.7% for undifferentiated histology, 88.0% for submucosal invasion, 87.9% for lymphovascular invasion (LVI), and 92.7% for lymph node metastasis (LNM), using endoscopic videos. The area under the curve values of the model were 0.992 for undifferentiated histology, 0.902 for submucosal invasion, 0.706 for LVI, and 0.680 for LNM in the test. In addition, the model showed significantly higher accuracy than the experts in predicting undifferentiated histology (92.7% vs. 71.6%), submucosal invasion (87.3% vs. 72.6%), and LNM (87.7% vs. 72.3%). The external validation showed accuracies of 75.6% and 71.9% for undifferentiated histology and submucosal invasion, respectively.
CONCLUSIONS: AI may assist endoscopists with high predictive performance for differentiation status and invasion depth of EGC. Further research is needed to improve the detection of LVI and LNM.
摘要：
背景：根据内镜检查结果准确预测早期胃癌（EGC）的病理结果对于决定内镜和手术切除至关重要。本研究旨在开发一种人工智能（AI）模型，以使用白光内窥镜图像和视频评估EGC的综合病理特征。
方法：要训练模型，我们回顾性收集了4,336张图像,前瞻性纳入了153个接受内镜或手术切除的EGC患者的视频.使用一组互斥的260张图像和10个视频，对模型的性能进行了测试，并与16位内窥镜医师（9位专家和7位新手）的性能进行了比较。最后,我们使用来自其他机构的436张图像和89个视频进行了外部验证.
结果：培训后，该模型对未分化组织学的预测准确率为89.7%，88.0%为粘膜下浸润，淋巴管浸润（LVI）占87.9％，淋巴结转移（LNM）占92.7％，使用内窥镜视频。未分化组织学模型曲线下面积值为0.992,粘膜下浸润0.902，LVI为0.706，测试中的LNM和0.680。此外,该模型在预测未分化组织学方面的准确性明显高于专家(92.7%vs.71.6%），粘膜下浸润（87.3%vs.72.6%），和LNM（87.7%与72.3%）。外部验证显示未分化组织学和粘膜下浸润的准确率分别为75.6%和71.9%。分别。
结论：AI可以帮助内窥镜医师对EGC的分化状态和浸润深度具有较高的预测能力。需要进一步的研究来改进LVI和LNM的检测。
方法：要训练模型，我们回顾性收集了4,336张图像,前瞻性纳入了153个接受内镜或手术切除的EGC患者的视频.使用一组互斥的260张图像和10个视频，对模型的性能进行了测试，并与16位内窥镜医师（9位专家和7位新手）的性能进行了比较。最后,我们使用来自其他机构的436张图像和89个视频进行了外部验证.
结果：培训后，该模型对未分化组织学的预测准确率为89.7%，88.0%为粘膜下浸润，淋巴管浸润（LVI）占87.9％，淋巴结转移（LNM）占92.7％，使用内窥镜视频。未分化组织学模型曲线下面积值为0.992,粘膜下浸润0.902，LVI为0.706，测试中的LNM和0.680。此外,该模型在预测未分化组织学方面的准确性明显高于专家(92.7%vs.71.6%），粘膜下浸润（87.3%vs.72.6%），和LNM（87.7%与72.3%）。外部验证显示未分化组织学和粘膜下浸润的准确率分别为75.6%和71.9%。分别。
结论：AI可以帮助内窥镜医师对EGC的分化状态和浸润深度具有较高的预测能力。需要进一步的研究来改进LVI和LNM的检测。
