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Abstract：
The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.
摘要：
近年来，已经报道了应用深度学习算法从苏木精和伊红（H＆E）染色的幻灯片的数字图像中预测各种癌症的分子谱。主要用于胃癌和结肠癌。在这项研究中,我们调查了H&E染色的子宫内膜癌载玻片图像预测相关错配修复(MMR)状态的潜在用途.收集127例子宫内膜癌原发灶的H＆E染色载玻片图像。使用Nanozoomer虚拟载玻片扫描仪（滨松光子学）进行数字化后，我们将扫描图像分割成5397个512×512像素的瓷砖。MMR蛋白(PMS2,MSH6)进行免疫组织化学染色,分为MMR熟练/缺陷，并为每个案例和瓷砖注释。我们训练了几个神经网络，包括卷积和基于注意力的网络，使用带有MMR状态注释的图块。在测试的网络中，ResNet50显示出用于预测MMR状态的接收器工作特征曲线（AUROC）下的最高面积为0.91。所构建的预测算法可适用于其他分子谱，并可用于在实施其他分子谱之前进行预筛选，更昂贵的基因分析测试。
