Background/Objectives: Breast cancer (BC) is the most prevalent cancer worldwide, with approximately 40% of early-stage BC patients developing recurrence despite initial treatments. Current diagnostic methods, such as mammography and solid tissue biopsies, face limitations in sensitivity, accessibility, and the ability to characterize tumor heterogeneity or monitor systemic disease progression.Methods: To address these gaps, this study investigates a fully automated analysis workflow using data derived from fluorescent Whole-Slide Imaging (fWSI) for detecting and classifying rare cells (circulating tumor and tumor microenvironment cells) in peripheral blood samples. Our methodology integrates supervised machine learning algorithms for rare event detection, immunofluorescence-based classification, and statistical quantification of cellular features.Results: Using a fWSI dataset of 534 cancer and non-cancer peripheral blood samples, the automated model demonstrated high concordance with manual annotation, achieving up to 98.9% accuracy and a precision-sensitivity AUC of 83.2%. Morphometric analysis of rare cells identified significant differences between normal donors, early-stage BC, and late-stage BC cohorts, with distinct clusters emerging in late-stage BC.Conclusions: These findings highlight the potential of liquid biopsy and algorithmic approaches for improving BC diagnostics and staging, offering a scalable, minimally invasive solution to enhance clinical decision-making. Future work aims to refine the automated framework to minimize errors and improve the robustness across diverse cohorts.
**背景/目的：** 乳腺癌是全球最常见的癌症，约40%的早期乳腺癌患者在初始治疗后出现复发。目前的诊断方法，如乳腺X线摄影和实体组织活检，在敏感性、可及性、表征肿瘤异质性或监测全身性疾病进展方面存在局限性。 **方法：** 为弥补这些不足，本研究探索了一种利用荧光全玻片成像数据，对外周血样本中稀有细胞（循环肿瘤细胞及肿瘤微环境细胞）进行检测与分类的全自动化分析流程。我们的方法整合了用于稀有事件检测的监督机器学习算法、基于免疫荧光的细胞分类以及细胞特征的统计学量化。 **结果：** 使用包含534份癌症与非癌症外周血样本的fWSI数据集，该自动化模型与人工标注结果高度一致，准确率高达98.9%，精确度-敏感度曲线下面积为83.2%。对稀有细胞的形态计量学分析显示，健康供体、早期乳腺癌和晚期乳腺癌队列之间存在显著差异，晚期乳腺癌队列中出现了独特的细胞簇。 **结论：** 这些发现凸显了液体活检与算法方法在改善乳腺癌诊断与分期方面的潜力，为增强临床决策提供了一种可扩展、微创的解决方案。未来的工作旨在完善自动化框架，以最小化误差并提高其在多样化队列中的稳健性。
Automated Single-Cell Analysis in the Liquid Biopsy of Breast Cancer
肿瘤(癌症)患者之家