Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015, USA.
Department of Bioengineering, Lehigh University, Bethlehem, PA, 18015, USA.
Sci Rep. 2020 Jul 22;10(1):12226. doi: 10.1038/s41598-020-69056-1.
Detection and characterization of rare circulating tumor cells (CTCs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis.
检测和鉴定患者血液中的稀有循环肿瘤细胞（CTC）对于癌症的诊断和监测至关重要。传统的通过荧光图像计数 CTC 的方法需要一系列繁琐的实验程序，并且经常影响细胞的活力。在这里，我们提出了一种利用明场显微镜图像数据分析的无标记 CTC 检测方法。该方法使用卷积神经网络（一种强大的图像分类和机器学习算法）对包含白细胞和 CTC 的患者血液样本的显微镜图像中检测到的细胞进行无标记分类。它需要最少的数据预处理，并且具有简单的实验设置。通过实验，我们表明，我们的方法可以在无需先进设备或专家用户的情况下，实现对稀有 CTC 的高准确率识别，从而为 CTC 的计数和鉴定提供更快、更简单的方法。随着未来更多数据的出现，机器学习模型可以进一步改进，并成为 CTC 分析的准确易用工具。
