Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
Department of Medical Data Science, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
Int J Mol Sci. 2020 Apr 30;21(9):3166. doi: 10.3390/ijms21093166.
It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system.
已知单个或孤立的肿瘤细胞会进入癌症患者的循环系统。这些循环肿瘤细胞（CTC）被认为是诊断癌症恶性程度的有效工具。然而，处理 CTC 样本和评估 CTC 序列分析结果具有挑战性。最近，卷积神经网络（CNN）模型作为一种深度学习模型，已越来越多地应用于医学图像分析。然而，利用机器学习方法是否能够在单细胞水平上识别细胞特征仍存在争议。本研究旨在验证基于培养过程中的细胞形态，人工智能系统是否可以对抗癌药物的敏感性进行分类。我们构建了一个基于 VGG16 模型的 CNN，可在单细胞水平上预测抗肿瘤药物的效率。机器学习结果表明，我们的模型可以识别抗肿瘤药物的效果，准确率约为 0.80。我们的结果表明，未来通过从血液中提取 CTC 并使用人工智能系统进行分类，可能实现精准医学，为个体患者识别有效的抗肿瘤药物。
