Carreras Joaquim, Roncador Giovanna, Hamoudi Rifat
Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Japan.
Monoclonal Antibodies Unit, Spanish National Cancer Research Center (CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, Spain.
Cancers (Basel). 2024 Dec 19;16(24):4230. doi: 10.3390/cancers16244230.
Ulcerative colitis is a chronic inflammatory bowel disease of the colon mucosa associated with a higher risk of colorectal cancer.
This study classified hematoxylin and eosin (H&E) histological images of ulcerative colitis, normal colon, and colorectal cancer using artificial intelligence (deep learning).
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.
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 (all values < 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.
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.
溃疡性结肠炎是一种结肠黏膜的慢性炎症性肠病，与结直肠癌风险较高相关。
本研究使用人工智能（深度学习）对溃疡性结肠炎、正常结肠和结直肠癌的苏木精和伊红（H&E）组织学图像进行分类。
设计并训练了一个卷积神经网络（CNN）来对三种诊断类型进行分类，包括35例溃疡性结肠炎（n = 9281个切片）、21例结肠对照（n = 12246）和18例结直肠癌（n = 63725）。数据被划分为训练集（70%）和验证集（10%）用于训练网络，以及测试集（20%）用于测试新数据上的性能。这些CNN包括从ResNet - 18进行迁移学习，并与其他CNN模型进行了比较。使用Grad - CAM技术进行可解释的计算机视觉人工智能，并在溃疡性结肠炎中进行了额外的LAIR1和TOX2免疫组织化学分析以分析免疫微环境。
传统临床病理分析表明，与美沙拉嗪反应性溃疡性结肠炎相比，需要类固醇治疗的溃疡性结肠炎的特征在于内镜下Baron评分和组织学Geboes评分更高，固有层中LAIR1表达更高，但孤立淋巴滤泡中TOX2表达更低（所有值<0.05）。CNN对溃疡性结肠炎的分类准确率为99.1%，对结直肠癌为99.8%，对结肠对照为99.1%。Grad - CAM热图确认了图像中哪些区域最重要。这些CNN还根据H&E、LAIR1和TOX2染色区分了需要类固醇治疗的溃疡性结肠炎和美沙拉嗪反应性溃疡性结肠炎。另外10例新的结直肠癌（腺癌）病例被正确分类。
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是溃疡性结肠炎相关的免疫肿瘤学标志物。
