许多研究表明，细胞形态学可以用于区分混杂在血液样本中的肿瘤细胞。然而，大多数的验证实验只包括同质细胞系，并未充分捕捉到癌细胞广泛的形态异质性。此外，由于其形态与血细胞不同，正常的非血细胞可能被错误地分类为癌细胞。在这里，我们构建了一个包含多种形态的器官样品派生癌细胞和正常细胞的显微镜图像数据集，并开发了一个概念验证的深度学习模型，可以在未标记的显微镜图像中区分癌细胞和正常细胞。总共收集了来自3例胆管癌患者的超过75,000个器官样品派生细胞。该模型在接收者操作特性曲线下的面积（AUROC）为0.78，并且能够将其推广应用到来自未见过患者的细胞图像上。这些资源为循环肿瘤细胞检测的自动化、鲁棒平台奠定了基础。© 2023年 Springer Nature 有限公司。

Many studies have shown that cellular morphology can be used to distinguish spiked-in tumor cells in blood sample background. However, most validation experiments included only homogeneous cell lines and inadequately captured the broad morphological heterogeneity of cancer cells. Furthermore, normal, non-blood cells could be erroneously classified as cancer because their morphology differ from blood cells. Here, we constructed a dataset of microscopic images of organoid-derived cancer and normal cell with diverse morphology and developed a proof-of-concept deep learning model that can distinguish cancer cells from normal cells within an unlabeled microscopy image. In total, more than 75,000 organoid-drived cells from 3 cholangiocarcinoma patients were collected. The model achieved an area under the receiver operating characteristics curve (AUROC) of 0.78 and can generalize to cell images from an unseen patient. These resources serve as a foundation for an automated, robust platform for circulating tumor cell detection.© 2023. Springer Nature Limited.