Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites.
腹水细胞学检查是诊断腹腔转移性结直肠癌（CRC）的一种经济有效的方法。然而，基于活检结果诊断腹膜转移癌较为困难，且腹水穿刺细胞学检查的敏感性和特异性较低，观察者间差异较大。本研究旨在应用人工智能（AI）技术，通过深度卷积神经网络对转移性结直肠癌腹水细胞学切片图像中的良恶性细胞进行分类。数据集来源于全国性AI研究细胞学数据库——开放AI数据集项目。用于训练、验证和测试的切片图像数量分别为56,560张、7,068张和6,534张。我们评估了1,041张腹水良性与转移性结直肠癌切片图像，以比较病理学家与AI算法的诊断性能，并检验AI辅助是否能够提高病理学家的诊断准确性。该AI方法在鉴别良恶性腹水时准确率、敏感性和特异性分别为93.74%、87.76%和99.75%。在AI辅助下，病理学家的诊断准确率从86.8%提升至90.5%，敏感性从73.3%提高至79.3%。所提出的深度学习方法可辅助不同经验水平的病理学家诊断腹水中的转移性结直肠癌细胞。
Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid
肿瘤(癌症)患者之家