Technologies for label-free separation of circulating tumor cells: from historical foundations to recent developments
Bibliographic record
Abstract
Circulating tumor cells (CTCs) are malignant cells shed into the bloodstream from a tumor that have the potential to establish metastases in different anatomical sites. The separation and subsequent characterization of these cells is emerging as an important tool for both biomarker discovery and the elucidation of mechanisms of metastasis. Established methods for separating CTCs rely on biochemical markers of epithelial cells that are known to be unreliable because of epithelial-to-mesenchymal transition, which reduces expression for epithelial markers. Emerging label-free separation methods based on the biophysical and biomechanical properties of CTCs have the potential to address this key shortcoming and present greater flexibility in the subsequent characterization of these cells. In this review we first present what is known about the biophysical and biomechanical properties of CTCs from historical studies and recent research. We then review biophysical label-free technologies that have been developed for CTC separation, including techniques based on filtration, hydrodynamic chromatography, and dielectrophoresis. Finally, we evaluate these separation methods and discuss requirements for subsequent characterization of CTCs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".