School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Lab Chip. 2022 Sep 13;22(18):3390-3401. doi: 10.1039/d2lc00190j.
Identification and enumeration of circulating tumor cells (CTCs) in peripheral blood are proved to correlate with the progress of metastatic cancer and can provide valuable information for diagnosis and monitoring of cancer. Here, we introduce a bright-field image cytometry (BFIC) technique, assisted by a multi-frame image correlation (MFIC) algorithm, as a label-free approach for tumor cell detection in peripheral blood. For this method, images of flowing cells in a wide channel were continuously recorded and cell types were determined simultaneously using a deep neural network of YOLO-V4 with an average precision (AP) of 98.63%, 99.04%, and 98.95% for cancer cell lines HT29, A549, and KYSE30, respectively. The use of the wide microfluidic channel (400 μm width) allowed for a high throughput of 50 000 cells per min without clogging. Then erroneous or missed cell classifications caused by imaging angle differences or accidental misinterpretations in single frames were corrected by the multi-frame correlation analysis. This further improved the AP to 99.40%, 99.52%, and 99.47% for HT29, A549, and KYSE30, respectively. Meanwhile, cell counting was also accomplished in this dynamic process. Moreover, our imaging cytometry method can readily detect as few as 10 tumor cells from 100 000 white blood cells and was unaffected by the EMT process. Furthermore, CTCs from 8 advanced-stage cancer clinical samples were also successfully detected, while none for 6 healthy control subjects. Although this method is implemented for CTCs, it can also be used for the detection of other rare cells.
循环肿瘤细胞（CTC）在血液中的鉴定和计数已被证明与转移性癌症的进展相关，并可为癌症的诊断和监测提供有价值的信息。在这里，我们介绍了一种明场图像细胞计数（BFIC）技术，该技术由多帧图像相关（MFIC）算法辅助，是一种用于外周血肿瘤细胞检测的无标记方法。对于该方法，使用 YOLO-V4 的深度学习神经网络以 98.63%、99.04%和 98.95%的平均精度（AP）对 HT29、A549 和 KYSE30 癌细胞系进行同时连续记录宽通道中流动细胞的图像和细胞类型的确定。使用 400μm 宽的宽微流道可实现 50,000 个细胞/分钟的高通量，而不会发生堵塞。然后，通过多帧相关分析纠正由成像角度差异或单个帧中的偶然错误解释引起的错误或错过的细胞分类。这进一步将 AP 提高到 HT29、A549 和 KYSE30 的 99.40%、99.52%和 99.47%。同时，在这个动态过程中也完成了细胞计数。此外，我们的成像细胞计数方法可以从 100,000 个白细胞中轻易地检测到低至 10 个肿瘤细胞，并且不受 EMT 过程的影响。此外，还成功地从 8 个晚期癌症临床样本中检测到了 CTC，而 6 个健康对照者中均未检测到。尽管该方法是针对 CTC 实施的，但它也可以用于其他稀有细胞的检测。
