A review of enrichment methods for circulating tumor cells: from single modality to hybrid modality
Bibliographic record
Abstract
Circulating tumor cell (CTC) analysis as a liquid biopsy can be used for early diagnosis of cancer, evaluating cancer progression, and assessing treatment efficacy. The enrichment of CTCs from patient blood is important for CTC analysis due to the extreme rarity of CTCs. This paper updates recent advances in CTC enrichment methods. We first review single-modality methods, including biophysical and biochemical methods. Hybrid-modality methods, combining at least two single-modality methods, are gaining increasing popularity for their improved performance. Then this paper reviews hybrid-modality methods, which are categorized into integrated and sequenced hybrid-modality methods. The state of the art indicates that the CTC capture efficiencies of integrated hybrid-modality methods can reach 85% or higher by taking advantage of the superimposed and enhanced capture effects from multiple single-modality methods. Moreover, a hybrid method integrating biophysical with biochemical methods is characterized by both high processing rate and high specificity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".