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Updated: Aug 24, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
A novel imaging-based approach for large extracellular vesicle detection and prognostic stratification in metastatic
Eszter Papp1, Vincent Liégeois2, Jo Vandesompele3
1CellCarta NV, Antwerp, Belgium.
Background:
Large extracellular vesicles (LEVs) are membrane-bound extracellular particles released by tumour cells into body fluids. Circulating LEVs carry tumour-associated biomaterials and are more abundant than circulating tumour cells (CTCs), representing a valuable liquid biopsy analyte. We evaluated the RareCyte® CTC platform's ability to identify LEVs in blood smears and explored associations with clinicopathological features in a metastatic breast cancer (MBC) cohort.
Methods:
MBC patient samples (N = 72) from a previously published prospective comparison study were retrospectively re-analysed to develop and validate an LEV identification and enumeration workflow. Correlations with CellSearch® tumour-derived extracellular vesicle (tdEV) counts and CTC counts were assessed using Spearman's rho. Between-platform comparisons were performed using Wilcoxon's signed-rank test Associations with overall survival (OS) were assessed using Kaplan-Meier analysis and Cox proportional hazards models. Overall survival was defined as the interval between blood collection and death from any cause.
Results:
Median LEV count [IQR] was 89 [53-208], with detectable LEVs in patients with low or absent CTC counts. LEV counts positively correlated with matched CTC counts (Spearman's ρ = 0.88, p < 0.001) and CellSearch tdEV counts (Spearman's ρ = 0.80, p < 0.001). Higher LEV counts were associated with significantly shorter overall survival (p = 0.04). RareCyte LEV counts were significantly higher than CellSearch tdEV counts, potentially reflecting methodological differences between enrichment workflows. In Cox analysis, higher LEV counts were associated with shorter OS (HR per 100 LEVs = 1.04, p = 0.0015), and CTCs were also significantly associated with OS.
Conclusion:
We developed an operational workflow for LEV detection and quantification using the RareCyte® CTC analysis platform. The inclusion of LEVs into CTC-based liquid biopsy analyses may provide complementary biomarker information, particularly in patients with low CTC burden.
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