Background: Ulcerative colitis is a chronic inflammatory bowel disease of the colon mucosa associated with a higher risk of colorectal cancer. Objective: This study classified hematoxylin and eosin (H&E) histological images of ulcerative colitis, normal colon, and colorectal cancer using artificial intelligence (deep learning). Methods: A convolutional neural network (CNN) was designed and trained to classify the three types of diagnosis, including 35 cases of ulcerative colitis (n = 9281 patches), 21 colon control (n = 12,246), and 18 colorectal cancer (n = 63,725). The data were partitioned into training (70%) and validation sets (10%) for training the network, and a test set (20%) to test the performance on the new data. The CNNs included transfer learning from ResNet-18, and a comparison with other CNN models was performed. Explainable artificial intelligence for computer vision was used with the Grad-CAM technique, and additional LAIR1 and TOX2 immunohistochemistry was performed in ulcerative colitis to analyze the immune microenvironment. Results: Conventional clinicopathological analysis showed that steroid-requiring ulcerative colitis was characterized by higher endoscopic Baron and histologic Geboes scores and LAIR1 expression in the lamina propria, but lower TOX2 expression in isolated lymphoid follicles (allpvalues < 0.05) compared to mesalazine-responsive ulcerative colitis. The CNN classification accuracy was 99.1% for ulcerative colitis, 99.8% for colorectal cancer, and 99.1% for colon control. The Grad-CAM heatmap confirmed which regions of the images were the most important. The CNNs also differentiated between steroid-requiring and mesalazine-responsive ulcerative colitis based on H&E, LAIR1, and TOX2 staining. Additional classification of 10 new cases of colorectal cancer (adenocarcinoma) were correctly classified. Conclusions: CNNs are especially suited for image classification in conditions such as ulcerative colitis and colorectal cancer; LAIR1 and TOX2 are relevant immuno-oncology markers in ulcerative colitis.
背景：溃疡性结肠炎是一种结肠黏膜的慢性炎症性肠病，与结直肠癌风险升高相关。目的：本研究采用人工智能（深度学习）技术对溃疡性结肠炎、正常结肠及结直肠癌的苏木精-伊红（H&E）组织学图像进行分类。方法：设计并训练卷积神经网络（CNN）对三类诊断进行分类，包括35例溃疡性结肠炎（n=9281个图像块）、21例结肠对照（n=12246个）及18例结直肠癌（n=63725个）。数据划分为训练集（70%）和验证集（10%）用于网络训练，测试集（20%）用于评估模型在新数据上的性能。CNN采用ResNet-18迁移学习，并与其他CNN模型进行比较。通过Grad-CAM技术实现计算机视觉的可解释人工智能分析，并对溃疡性结肠炎样本进行LAIR1和TOX2免疫组化染色以分析免疫微环境。结果：常规临床病理分析显示，与美沙拉嗪应答型溃疡性结肠炎相比，激素依赖型溃疡性结肠炎具有更高的内镜Baron评分和组织学Geboes评分，固有层LAIR1表达升高，但孤立淋巴滤泡中TOX2表达降低（所有p值<0.05）。CNN分类准确率达溃疡性结肠炎99.1%、结直肠癌99.8%、结肠对照99.1%。Grad-CAM热图确认了图像中最具鉴别意义的区域。基于H&E、LAIR1和TOX2染色，CNN还能区分激素依赖型与美沙拉嗪应答型溃疡性结肠炎。对10例新增结直肠癌（腺癌）病例的分类结果完全正确。结论：CNN特别适用于溃疡性结肠炎和结直肠癌等疾病的图像分类；LAIR1和TOX2是溃疡性结肠炎中相关的免疫肿瘤学标志物。
肿瘤(癌症)患者之家