Automated Single-Cell Analysis in the Liquid Biopsy of Breast Cancer

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.