Deep learning for automatic diagnosis of gastric dysplasia using whole-slide histopathology images in endoscopic specimens
Deep learning for automatic diagnosis of gastric dysplasia using whole-slide histopathology images in endoscopic specimens
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DOI：
10.1007/s10120-022-01294-w
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发表时间：
2022-04-08
期刊：
影响因子：
7.4
通讯作者：
Jin, Mulan
中科院分区：
文献类型：
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作者：
Shi, Zhongyue;Zhu, Chuang;Jin, Mulan
背景在内镜标本中，鉴别胃上皮再生改变与异型增生以及异型增生的组织病理学诊断在观察者之间存在分歧。在这项研究中，我们开发了一种方法来区分胃上皮再生变化与异型增生，并进一步细分异型增生。同时，利用领域自适应技术对跨医院诊断进行了优化。方法将来自两家医院的897张内镜标本的全切片图像（WSIs）分为训练组、内部验证组和外部验证组。我们开发了一种深度学习（DL）与DA（DLDA）模型，将胃异型增生和上皮再生变化分为三类：异型增生阴性（NFD）、低度异型增生（LGD）和高度异型增生（HGD）/粘膜内浸润瘤（IMN）。基于DLDA模型的诊断进行了比较，12名病理学家使用100例胃活检病例。结果在内部验证队列中，以受试者工作特征曲线下面积（AUC）的宏观平均值衡量的诊断性能为0.97。在独立的外部验证队列中，我们的DLDA模型将宏观平均AUC从0.67增加到0.82。就NFD和HGD病例而言，我们的模型的诊断敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）均显著高于初级和高级病理学家。我们的模型的诊断灵敏度，NPV，高于专业病理学家。结论我们的DLDA模型可以区分胃上皮再生改变和异型增生，并进一步对内镜标本中的异型增生进行分类。同时，实现了跨院诊断的显著改善。
Background Distinguishing gastric epithelial regeneration change from dysplasia and histopathological diagnosis of dysplasia is subject to interobserver disagreement in endoscopic specimens. In this study, we developed a method to distinguish gastric epithelial regeneration change from dysplasia and further subclassify dysplasia. Meanwhile, optimized the cross-hospital diagnosis using domain adaption (DA). Methods 897 whole slide images (WSIs) of endoscopic specimens from two hospitals were divided into training, internal validation, and external validation cohorts. We developed a deep learning (DL) with DA (DLDA) model to classify gastric dysplasia and epithelial regeneration change into three categories: negative for dysplasia (NFD), low-grade dysplasia (LGD), and high-grade dysplasia (HGD)/intramucosal invasion neoplasia (IMN). The diagnosis based on the DLDA model was compared to 12 pathologists using 100 gastric biopsy cases. Results In the internal validation cohort, the diagnostic performance measured by the macro-averaged area under the receiver operating characteristic curve (AUC) was 0.97. In the independent external validation cohort, our DLDA models increased macro-averaged AUC from 0.67 to 0.82. In terms of the NFD and HGD cases, our model's diagnostic sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were significantly higher than junior and senior pathologists. Our model's diagnostic sensitivity, NPV, was higher than specialist pathologists. Conclusions We demonstrated that our DLDA model could distinguish gastric epithelial regeneration change from dysplasia and further subclassify dysplasia in endoscopic specimens. Meanwhile, achieved significant improvement of diagnosis cross-hospital.