Growing indication for FNA to study and analyze tumor heterogeneity at metastatic sites
Bibliographic record
Abstract
In routine practice, suspected metastases in patients with cancer are only occasionally biopsied, primarily because of the cost and invasiveness of the procedure. However, biopsies of metastatic lesions can be valuable, not only in confirming the presence of metastatic disease, but also in revealing unsuspected benign disease or secondary malignancies. In addition, such biopsies also allow the assessment of biomarkers that might differ from those on primary tumor cells, and can thereby facilitate selection of the optimal treatment. Because of the increasing recognition of clonal and phenotypic heterogeneity of tumors, we anticipate that in the near future, biopsying of metastatic lesions will constitute a standard-of-care practice, allowing assessment of molecular differences between the primary tumor and metastatic lesions. In our opinion, fine-needle aspiration is currently the best method for making repeated biopsies to monitor the tumor: it is minimally invasive, safe, and cost effective and can be coupled with modern ancillary techniques. Here we provide an up-to-date review of the clinical implications of tumor heterogeneity in metastatic disease and the ancillary molecular techniques used in cytology; we also discuss the role of modern cytology in contemporary diagnosis and management of metastatic 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".