New advances in circulating tumor cell‑mediated metastasis of breast cancer (Review)
Bibliographic record
Abstract
Breast cancer stands as the most prevalent form of cancer affecting women, with metastasis serving as a leading cause of mortality among patients with breast cancer. Gaining a comprehensive understanding of the metastatic mechanism in breast cancer is essential for early detection and precision treatment of the disease. Circulating tumor cells (CTCs) play a vital role in this context, representing cancer cells that detach from tumor tissues and enter the bloodstream of cancer patients. These cells travel in the blood circulation as single cells or clusters. Recent research has shed light on the enhanced metastatic potential of CTC clusters compared to single CTCs, despite their limited occurrence. The aim of the present review was to explore recent findings on CTCs with a particular focus on the clustering phenomenon of CTCs observed in breast cancer. Additionally, the present review delved into the comparison between single CTCs and CTC clusters regarding their implications for the treatment and prognosis of patients diagnosed with metastatic breast cancer. By examining the role and mechanisms of CTCs in breast cancer metastasis, the present review provided an improved understanding of CTCs and their significance in early detection of breast cancer metastasis through peripheral blood analysis. Moreover, it contributed to the comprehension of cancer prognosis and prediction by highlighting the implications of CTCs in these aspects. Ultimately, the present study seeks to advance knowledge in the field and pave the way for improved approaches to breast cancer management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".