Topological Data Analysis for Robust Classification of Circulating Cancer Cells

Accurate detection and classification of rare cancer cells in the bloodstream are critical for early cancer diagnosis, monitoring metastasis, and assessing treatment response. However, due to their rarity, heterogeneous morphology, and potential overlap with other cell types, reliably distinguishing these cancer cells remains a significant challenge. To address these issues, we propose a novel method that combines deep learning-based classifiers with topological data analysis to improve classification accuracy. Our approach uses persistent homology to differentiate between intact and apoptotic cells based on their intrinsic topological features. This method significantly enhances classification performance, even with limited data or challenging image acquisition conditions. By incorporating proprietary reagents, state-of-the-art vision-based neural networks, and topological analysis, our approach effectively tackles the challenges of structural variability and occlusion, outperforming traditional methods in complex and noisy scenarios. This integrated framework offers a more accurate, robust and efficient solution for cancer cell detection, recognition and categorization, with potential applications in cancer diagnostics and therapeutic monitoring.