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Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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 (p = 0.024) and 1p19q co-deletion status (p = 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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 (p = 0.024) and 1p19q co-deletion status (p = 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.
UNASSIGNED: 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.
摘要：
■循环肿瘤细胞（CTC）的检测是肿瘤管理中的一项有前途的技术；然而，CTC鉴定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的主要问题，CTC检测目前具有挑战性。为了提高胶质瘤CTCs的识别效率,我们开发了一种基于核质比（KR）的识别方法，并构建了一种自动识别算法。我们还打算确定高KRCTC与患者临床特征之间的相关性。
■从68例神经胶质瘤患者的外周血样本中分离CTC，并使用DNA-seq和免疫荧光染色进行分析。随后，我们收集神经胶质瘤患者和配对个体的临床信息进行分析.进行ROC曲线以评估基于KR的鉴定方法的效率。最后,捕获CTC图像并将其用于开发CTC识别算法。
■KR是识别神经胶质瘤CTC比细胞大小更好的参数。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，强调其在预测少突胶质细胞瘤中的效用（曲线下面积=0.770）。准确性,灵敏度,我们的算法的特异性为93.4%，81.0%,和97.4%，分别,而精密度和F1评分分别为90.9%和85.7%，分别。
■我们的发现显着提高了检测神经胶质瘤CTC的效率，并揭示了CTC计数与患者临床特征之间的相关性。这将允许研究人员进一步研究CTC的临床效用。此外，我们的自动识别算法可以在CTC识别过程中保持较高的精度，缩短时间和成本，并显著减轻临床医生的负担。
■从68例神经胶质瘤患者的外周血样本中分离CTC，并使用DNA-seq和免疫荧光染色进行分析。随后，我们收集神经胶质瘤患者和配对个体的临床信息进行分析.进行ROC曲线以评估基于KR的鉴定方法的效率。最后,捕获CTC图像并将其用于开发CTC识别算法。
■KR是识别神经胶质瘤CTC比细胞大小更好的参数。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，强调其在预测少突胶质细胞瘤中的效用（曲线下面积=0.770）。准确性,灵敏度,我们的算法的特异性为93.4%，81.0%,和97.4%，分别,而精密度和F1评分分别为90.9%和85.7%，分别。
■我们的发现显着提高了检测神经胶质瘤CTC的效率，并揭示了CTC计数与患者临床特征之间的相关性。这将允许研究人员进一步研究CTC的临床效用。此外，我们的自动识别算法可以在CTC识别过程中保持较高的精度，缩短时间和成本，并显著减轻临床医生的负担。
