Circulating Tumor Cells: A Window to Understand Cancer Metastasis, Monitor and Fight Against Cancers
Bibliographic record
Abstract
Metastases are the major culprits behind most cancer-related death and the central challenge to the eradication of a malignancy. Circulating tumor cells (CTCs) have the potential to help us understand how metastases form, to be utilized for cancer diagnosis and treatment selection and even to be targeted for cancer treatment. Many advances have been made regarding the isolation of these rare cells. However, several challenges and limitations in CTC analysis still exist. Multiple color immunofluorescence, genetic analysis (e.g. Fluorescence in situ Hybridization, microarray and next generation sequencing) and CTC culture will be effective tools to study CTCs and provide information on metastatic mechanism and clinical implication. In this review, we discuss the importance of CTC study in understanding cancer metastasis and their potential clinical application as biomarkers to predict cancer progression and treatment response, as well as the current situation for CTC isolation and analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".