Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
Int J Mol Sci. 2020 Sep 11;21(18):6652. doi: 10.3390/ijms21186652.
Gastritis is a prevalent disease and commonly classified into autoimmune (A), bacterial (B), and chemical (C) type gastritis. While the former two subtypes are associated with an increased risk of developing gastric intestinal adenocarcinoma, the latter subtype is not. In this study, we evaluated the capability to classify common gastritis subtypes using convolutional neuronal networks on a small dataset of antrum and corpus biopsies.
1230 representative 500 × 500 µm images of 135 patients with type A, type B, and type C gastritis were extracted from scanned histological slides. Patients were allocated randomly into a training set (60%), a validation set (20%), and a test set (20%). One classifier for antrum and one classifier for corpus were trained and optimized. After optimization, the test set was analyzed using a joint result from both classifiers.
Overall accuracy in the test set was 84% and was particularly high for type B gastritis with a sensitivity of 100% and a specificity of 93%.
Classification of gastritis subtypes is possible using convolutional neural networks on a small dataset of histopathological images of antrum and corpus biopsies. Deep learning strategies to support routine diagnostic pathology merit further evaluation.
胃炎是一种常见疾病，通常分为自身免疫性（A）、细菌性（B）和化学性（C）胃炎。前两种亚型与胃肠腺癌风险增加相关，而后一种亚型则不然。本研究旨在评估卷积神经网络在小样本胃窦和胃体活检组织学图像上对常见胃炎亚型进行分类的能力。
从扫描的组织学载玻片上提取了 135 例 A、B 和 C 型胃炎患者的 1230 张具有代表性的 500×500μm 图像。患者被随机分配到训练集（60%）、验证集（20%）和测试集（20%）。分别为胃窦和胃体训练和优化了一个分类器。优化后，使用两个分类器的联合结果分析测试集。
测试集中的总体准确率为 84%，B 型胃炎的准确率特别高，敏感性为 100%，特异性为 93%。
使用胃窦和胃体活检组织学图像的小数据集，通过卷积神经网络对胃炎亚型进行分类是可行的。支持常规诊断病理学的深度学习策略值得进一步评估。
