Interpretatively automated identification of circulating tumor cells from human peripheral blood with high performance.
Interpretatively automated identification of circulating tumor cells from human peripheral blood with high performance.
复制标题
高效的人类外周血循环肿瘤细胞的解释性自动鉴定。
DOI：
10.3389/fbioe.2023.1013107
复制
发表时间：
2023
影响因子：
5.7
通讯作者：
Liu, Wanyu
中科院分区：
文献类型：
--
作者：
Li, Xiaolei;Chen, Mingcan;Xu, Jingjing;Wu, Dihang;Ye, Mengxue;Wang, Chi;Liu, Wanyu
关键词：
循环肿瘤细胞（CTC）的检测和分析将有助于精确的癌症诊断和有效的预后评估。然而，传统方法严重依赖于根据物理或生物学特征分离CTC，劳动强度大，因此不适合快速检测。此外，目前可用的智能方法缺乏可解释性，这在诊断过程中产生了很多不确定性。因此，我们在这里提出了一种利用高分辨率明场显微图像的自动化方法，以深入了解细胞模式。具体来说，通过使用基于优化的单次多盒检测器（SSD）的神经网络，并集成注意力机制和特征融合模块，实现了CTC的精确识别。与传统的SSD系统相比，我们的方法表现出优越的检测性能，召回率为92.2%，最大平均精度（AP）值为97.9%。值得注意的是，基于 SSD 的最优神经网络与先进的可视化技术相结合，即用于模型解释的梯度加权类激活映射（Grad-CAM）和用于数据可视化的 t 分布随机邻域嵌入（T-SNE）。我们的工作首次展示了基于SSD的神经网络在人类外周血液环境中识别CTC的出色性能，显示出早期检测和持续监测癌症进展的巨大潜力。
The detection and analysis of circulating tumor cells (CTCs) would be of aid in a precise cancer diagnosis and an efficient prognosis assessment. However, traditional methods that rely heavily on the isolation of CTCs based on their physical or biological features suffer from intensive labor, thus being unsuitable for rapid detection. Furthermore, currently available intelligent methods are short of interpretability, which creates a lot of uncertainty during diagnosis. Therefore, we propose here an automated method that takes advantage of bright-field microscopic images with high resolution, so as to take an insight into cell patterns. Specifically, the precise identification of CTCs was achieved by using an optimized single-shot multi-box detector (SSD)–based neural network with integrated attention mechanism and feature fusion modules. Compared to the conventional SSD system, our method exhibited a superior detection performance with the recall rate of 92.2%, and the maximum average precision (AP) value of 97.9%. To note, the optimal SSD-based neural network was combined with advanced visualization technology, i.e., the gradient-weighted class activation mapping (Grad-CAM) for model interpretation, and the t-distributed stochastic neighbor embedding (T-SNE) for data visualization. Our work demonstrates for the first time the outstanding performance of SSD-based neural network for CTCs identification in human peripheral blood environment, showing great potential for the early detection and continuous monitoring of cancer progression.
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DOI：
10.1109/tpami.2018.2858826
发表时间：
2020-02-01
影响因子：
23.6
作者：
Lin, Tsung-Yi;Goyal, Priya;Dollar, Piotr
通讯作者：
Dollar, Piotr
影响因子：
3.5
作者：
Wittekind, C;Neid, M
通讯作者：
Neid, M
DOI：
10.3390/s21103569
发表时间：
2021-05-20
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Magalhães SA;Castro L;Moreira G;Dos Santos FN;Cunha M;Dias J;Moreira AP
通讯作者：
Moreira AP
影响因子：
64.8
作者：
Massague, Joan;Obenauf, Anna C.
通讯作者：
Obenauf, Anna C.
影响因子：
4.1
作者：
Svensson, Carl-Magnus;Huebler, Ron;Figge, Marc Thilo
通讯作者：
Figge, Marc Thilo