Metastatic breast cancer: mechanisms and opportunities for cytology
Bibliographic record
Abstract
Despite significant advances in diagnosis, surgical techniques, general patient care, and local and systemic adjuvant therapies, metastatic disease remains the most critical condition limiting the survival of patients with breast cancer. Therefore, the development of effective treatment against late-arising metastasis has become the centre of clinical attention and is one of the current challenges in cancer research. A deeper understanding of the metastatic cascade is fundamental, and the need for repetitive tumour assessments for the evaluation of tumour evolution is a relatively new practice in routine medical care. As such, fine needle aspiration cytology (FNAC) is ideally placed to monitor biological changes in metastasis that may affect treatment and response. As FNAC is a minimally invasive method, it can be performed repeatedly with relatively little trauma, and selective ancillary tests can be applied to FNAC specimens, including for tumour whose primary nature is known. Herein, we review how the linear and parallel models explain metastatic dissemination, thus influencing therapeutic and clinical decisions, and how cytology, together with immunocytochemistry and molecular analysis, can be a tool for routine clinical practice and clinical trials aimed at metastatic disease with a special emphasis on breast cancer.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".