利用智能成像流式细胞仪诊断浆液性积液

Mengping Long, Yueyun Weng, Liye Mei, Dingchao Yang, Shubin Wei, Guanxiong Meng, Wanyue Zhao, Sheng Liu, Du Wang, Yiqiang Liu, Hui Shen, Jianxuan Hou, Yu Xu, Liang Tao, Fuling Zhou, Hongwei Chen, Taobo Hu, Cheng Lei
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摘要

浆液性渗出是指在浆液腔(包括胸膜腔、腹膜腔和心包腔)中积聚的额外液体。在细胞病理学中区分良性反应性渗出液和恶性增生引起的渗出液非常重要,因为不同的诊断会导致完全不同的疾病分期和治疗选择。传统的细胞病理学程序具有低通量和低客观性的缺点。为了提高恶性浆液性渗出诊断的效率和准确性,本文首先采用了一种名为光流体时间拉伸显微镜的成像流式细胞仪,以每秒 100 000 次的事件发生率和优于 1 微米的空间分辨率对浆液性渗出中的细胞进行成像。然后利用卷积神经网络对获取的细胞图像进行分析,从而准确检测出恶性细胞。18 个临床样本(包括 14 个恶性样本和 4 个良性样本)验证了该方法的性能。结果表明,该方法检测恶性细胞的准确率高达 90.53%。该方法的高通量、高准确性和高便利性使其成为各种情况下恶性浆液性渗出诊断的潜在解决方案。
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Diagnosis of Serous Effusion with Intelligent Imaging Flow Cytometry

A serous effusion is a buildup of extra fluid in the serous cavities including pleural, peritoneal, and pericardial cavities. It is important to distinguish benign reactive effusions from effusions caused by malignant proliferation in cytopathology since different diagnoses can lead to completely different disease staging and therapeutic choices. The conventional cytopathology procedure has the disadvantages of low throughput and low objectivity. To enhance the efficiency and accuracy of malignant serous effusion diagnosis, in this paper, an imaging flow cytometry, called optofluidic time-stretch microscopy is first employed, to image the cells in the serous effusion at an event rate of 100 000 events per second and with a spatial resolution better than 1 µm. The acquired cellular images are then analyzed using a convolutional neural network, by which the malignant cells are accurately detected. The performance of the method is validated with 18 clinical samples, including 14 malignant and 4 benign ones. The results show that the method can detect malignant cells at an accuracy of 90.53%. The high throughput, high accuracy, and high convenience of the method make it a potential solution for malignant serous effusion diagnosis in various scenarios.

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