China Academy of Information and Communications Technology, Beijing, China.
Zhuhai Sanmed Biotech Ltd, Zhuhai, Guangdong, China.
Cytometry A. 2023 Mar;103(3):227-239. doi: 10.1002/cyto.a.24682. Epub 2022 Aug 15.
Recent studies have suggested that circulating tumor cells with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood samples, such as circulating genetically abnormal cells (CACs), can be used as a non-invasive tool to detect patients with benign pulmonary nodules. These cells are identified through fluorescence signals counting by using 4-color fluorescence in situ hybridization (FISH) technology that exhibits high stability, sensitivity, and specificity. When FISH data are analyzed, the overlapping cells and fluorescence noise is a great challenge for identifying of CACs, thereby seriously affecting the efficiency of clinical diagnosis. To address this problem, in this study, we proposed an end-to-end FISH-based method (CACNET) for CAC identification. CACNET achieved nuclear segmentation and counted 4-color staining signals through improved Mask region-based convolutional neural network (R-CNN), followed by cell category (normal cell, deletion cell, gain cell, or CAC) according to pathological criteria. Firstly, the segmentation accuracy of overlapping nuclei was improved by adding an edge constraint head during training. Then, the interference of fluorescence noise was reduced by fusing non-local module to reconstruct the feature extraction network of Mask R-CNN. We trained and tested the proposed model on a dataset comprising 700 frames with 58,083 nuclei. The Accuracy, Sensitivity, and Specificity (overall performance metric for the algorithm) of CAC identification with CACNET were 94.06%, 92.1%, and 99.8%, respectively. Moreover, the developed method exhibited approximately identification speed of approximately 0.22 s per frames. The results showed that the proposed method outperformed the existing CAC identification methods, making it a promising approach for early screening of lung cancer.
最近的研究表明，从富含单核细胞的外周血样本中循环肿瘤细胞的基因拷贝数异常，如循环遗传异常细胞 (CAC)，可作为一种非侵入性工具来检测良性肺结节患者。这些细胞通过使用 4 色荧光原位杂交 (FISH) 技术的荧光信号计数来识别，该技术具有高稳定性、灵敏度和特异性。在分析 FISH 数据时，重叠细胞和荧光噪声是识别 CAC 的巨大挑战，从而严重影响临床诊断的效率。为了解决这个问题，在本研究中，我们提出了一种基于 FISH 的端到端方法 (CACNET) 来识别 CAC。CACNET 通过改进的基于掩模区域的卷积神经网络 (R-CNN) 进行核分割和计数 4 色染色信号，然后根据病理标准根据细胞类别（正常细胞、缺失细胞、增益细胞或 CAC）进行分类。首先，通过在训练过程中添加边缘约束头来提高重叠核的分割精度。然后，通过融合非局部模块来减少荧光噪声的干扰，重建 Mask R-CNN 的特征提取网络。我们在包含 700 个包含 58,083 个核的数据集上对所提出的模型进行了训练和测试。CACNET 识别 CAC 的准确性、灵敏度和特异性（算法的总体性能指标）分别为 94.06%、92.1%和 99.8%。此外，所开发的方法的识别速度约为 0.22 秒/帧。结果表明，该方法优于现有的 CAC 识别方法，是早期筛查肺癌的一种有前途的方法。
