Centrifugal microfluidic systems for cancer cell separation: Advances, challenges, and applications
Bibliographic record
Abstract
Cancer remains a leading global health challenge, with circulating tumor cells (CTCs) playing a pivotal role in metastasis and disease progression. Efficient detection and isolation of CTCs are essential for early diagnosis, therapeutic monitoring, and the advancement of personalized treatment strategies. However, their extreme rarity in peripheral blood presents significant technical challenges for reliable enrichment and analysis. Centrifugal microfluidic systems, or lab-on-a-disc (LOCD) platforms, offer a promising solution by enabling automated, high-throughput, and cost-effective separation of rare cancer cells with minimal manual intervention. This review provides a comprehensive analysis of recent advances in centrifugal microfluidic technologies, with a focus on cancer cell separation for diagnostics and the challenges of clinical translation. Particular attention is given to the optimization of separation techniques, improvements in microchannel design, and strategies to minimize contamination and cell damage while enhancing purity and yield. We critically compare label-free, affinity-based, and hybrid separation approaches, and examine how material selection, surface functionalization, automation, and integrated detection modules in-fluence device performance. Clinical relevance is emphasized throughout, including examples of real patient applications, regulatory challenges, and translational barriers. Furthermore, we propose future directions to address persistent limitations such as clogging, limited specificity, and standardization. While routine clinical implementation remains complex, recent innovations have significantly improved system robustness, reproducibility, and accessibility. This review serves as a resource for researchers and clinicians, summarizing the current state of the field and outlining the path forward for the next generation of centrifugal microfluidic systems tailored for cancer cell separation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".