Is thyroid gland only a “land” for primary malignancies? role of morphology and immunocytochemistry
Bibliographic record
Abstract
BACKGROUND: Thyroid Metastases (TM) represent a rare entity with an estimated variability ranging between 0 and 24%. Fine needle aspiration cytology (FNAC) might be useful in discriminating between primary and TM nodules especially when ancillary techniques (i.e., immunocytochemistry-ICC) are carried out. METHODS: We herein appraised a series of 20 TM on FNAC analyzed between 2000 and 2013. We included eight male and 12 female patients. The cytological cases were processed with both liquid based (LBC) and conventional cytology. RESULTS: We reported 2.2% TM out of 910 malignancies. Our TM cases resulted as: six lung (LG), five gastro-intestinal (GI), five breast (B), three larynx (LX), and one clear cell renal carcinoma (CCRC) metastases. All the patients had a previous known cancer history. Although the cytological features were likely to suspect a TM, the application of ICC panels was contributive in 100% cases. None of TMs resulted as a unique localization whereby two cases underwent total thyroidectomy (including one B and one CCRC) and 18 TM were treated with radio-chemotherapy approaches. CONCLUSIONS: FNAC empowered the diagnostic workup of patients with TM avoiding useless surgical approach. The low sensitivity of cytology might be reinforced by the application of ancillary techniques. In contrast with the reported predominant rate of kidney metastatic carcinomas, our findings underlined that intestinal cancer as well as lung and breast are the most common TM. TM are frequently multifocal and in a contest of a systemic disease so that a tailored therapy seems to be the best treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".