Zhu Xinyi, Wen Shen, Deng Shuhang, Wu Gao, Tian Ruyong, Hu Ping, Ye Liguo, Sun Qian, Xu Yang, Deng Gang, Zhang Dong, Yang Shuang, Qi Yangzhi, Chen Qianxue
Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.
School of Physics and Technology, Wuhan University, Wuhan, China.
Front Oncol. 2022 May 13;12:893769. doi: 10.3389/fonc.2022.893769. eCollection 2022.
Detection of circulating tumor cells (CTCs) is a promising technology in tumor management; however, the slow development of CTC identification methods hinders their clinical utility. Moreover, CTC detection is currently challenging owing to major issues such as isolation and correct identification. To improve the identification efficiency of glioma CTCs, we developed a karyoplasmic ratio (KR)-based identification method and constructed an automatic recognition algorithm. We also intended to determine the correlation between high-KR CTC and patients' clinical characteristics.
CTCs were isolated from the peripheral blood samples of 68 glioma patients and analyzed using DNA-seq and immunofluorescence staining. Subsequently, the clinical information of both glioma patients and matched individuals was collected for analyses. ROC curve was performed to evaluate the efficiency of the KR-based identification method. Finally, CTC images were captured and used for developing a CTC recognition algorithm.
KR was a better parameter than cell size for identifying glioma CTCs. We demonstrated that low CTC counts were independently associated with isocitrate dehydrogenase (IDH) mutations ( = 0.024) and 1p19q co-deletion status ( = 0.05), highlighting its utility in predicting oligodendroglioma (area under the curve = 0.770). The accuracy, sensitivity, and specificity of our algorithm were 93.4%, 81.0%, and 97.4%, respectively, whereas the precision and F1 score were 90.9% and 85.7%, respectively.
Our findings remarkably increased the efficiency of detecting glioma CTCs and revealed a correlation between CTC counts and patients' clinical characteristics. This will allow researchers to further investigate the clinical utility of CTCs. Moreover, our automatic recognition algorithm can maintain high precision in the CTC identification process, shorten the time and cost, and significantly reduce the burden on clinicians.
循环肿瘤细胞(CTC)检测是肿瘤管理中一项很有前景的技术;然而,CTC识别方法的缓慢发展阻碍了其临床应用。此外,由于诸如分离和正确识别等主要问题,目前CTC检测具有挑战性。为了提高胶质瘤CTC的识别效率,我们开发了一种基于核质比(KR)的识别方法并构建了自动识别算法。我们还旨在确定高KR CTC与患者临床特征之间的相关性。
从68例胶质瘤患者的外周血样本中分离出CTC,并使用DNA测序和免疫荧光染色进行分析。随后,收集胶质瘤患者和匹配个体的临床信息进行分析。绘制ROC曲线以评估基于KR的识别方法的效率。最后,采集CTC图像并用于开发CTC识别算法。
KR是比细胞大小更好的识别胶质瘤CTC的参数。我们证明低CTC计数与异柠檬酸脱氢酶(IDH)突变(P = 0.024)和1p19q共缺失状态(P = 0.05)独立相关,突出了其在预测少突胶质细胞瘤中的效用(曲线下面积 = 0.770)。我们算法的准确率、灵敏度和特异性分别为93.4%、81.0%和97.4%,而精确率和F1分数分别为90.9%和85.7%。
我们的研究结果显著提高了检测胶质瘤CTC的效率,并揭示了CTC计数与患者临床特征之间的相关性。这将使研究人员能够进一步研究CTC的临床应用。此外,我们的自动识别算法在CTC识别过程中可以保持高精度,缩短时间和成本,并显著减轻临床医生的负担。