自动检测分割分类稀有癌细胞，提升液体活检效率
Fully Automated CTC Detection, Segmentation and Classification for Multi-Channel IF Imaging
- 基于机器学习构建全流程自动化分析管道
- 99%灵敏度、97%特异性，处理15名患者9533个细胞
- 将数百万候选细胞压缩至335个，助力临床决策
液体活检（如血液采样）为监测转移性乳腺癌（mBCa）提供了比组织活检更微创、非局部化的替代方案。免疫荧光（IF）显微镜可对患者样本中的数百万血细胞进行成像与分析。通过检测并基因测序循环肿瘤细胞（CTCs），可为不同癌症亚型制定个性化治疗方案。然而，CTCs极为稀少（约1/200万），手动检测难度极大。此外，临床医生需依赖定量细胞生物标志物手动分类CTCs，这需要先完成细胞检测、分割和特征提取。为此，我们开发了一套全自动化机器学习驱动的生产级流程，可高效实现多通道IF图像中CTCs的检测、分割与分类。在15名mBCa患者的9,533个细胞上，该方法达到超过99%的敏感性和97%的特异性。该流程已成功部署于真实mBCa患者，平均将1400万检测细胞减少至仅335个CTC候选，显著降低人工审阅负担。
原文摘要 · Abstract (English)
Liquid biopsies (eg., blood draws) offer a less invasive and non-localized alternative to tissue biopsies for monitoring the progression of metastatic breast cancer (mBCa). Immunofluoresence (IF) microscopy is a tool to image and analyze millions of blood cells in a patient sample. By detecting and genetically sequencing circulating tumor cells (CTCs) in the blood, personalized treatment plans are achievable for various cancer subtypes. However, CTCs are rare (about 1 in 2M), making manual CTC detection very difficult. In addition, clinicians rely on quantitative cellular biomarkers to manually classify CTCs. This requires prior tasks of cell detection, segmentation and feature extraction. To assist clinicians, we have developed a fully automated machine learning-based production-level pipeline to efficiently detect, segment and classify CTCs in multi-channel IF images. We achieve over 99% sensitivity and 97% specificity on 9,533 cells from 15 mBCa patients. Our pipeline has been successfully deployed on real mBCa patients, reducing a patient average of 14M detected cells to only 335 CTC candidates for manual review.
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