New technologies for the detection of circulating tumour cells
Bibliographic record
Abstract
The vast majority of cancer-related death is due to the metastatic spread of the primary tumour. Circulating tumour cells (CTC) are essential for establishing metastasis and their detection has long been considered as a possible tool to assess the aggressiveness of a given tumour and its potential of subsequent growth at distant organs. Conventional markers are not reliable in detecting occult metastasis and, for example, fail to identify approximately 40% of cancer patients in need of more aggressive or better adjusted therapies. Recent studies in metastatic breast cancer have shown that CTC detection can be used as a marker for overall survival and assessment of the therapeutic response. The benefits of CTC detection in early breast cancer and other solid tumours need further validation. Moreover, optimal CTC detection techniques are the subject of controversy as several lack reproducibility, sensitivity and/or specificity. Recent technical advances allow CTC detection and characterization at the single-cell level in the blood or in the bone marrow. Their reproducibility propels the use of CTC in cancer staging and real-time monitoring of systemic anticancer therapies in several large clinical trials. CTC assays are being integrated in large clinical trials to establish their potential in the management of cancer patients and improve our understanding of metastasis biology. This review will focus on the techniques currently used, the technical advancements made, the limitations of CTC detection and future perspectives in this field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".