Clinical application of human telomerase reverse transcriptase gene expression in thyroid follicular tumors by fine-needle aspirations using in situ hybridization
Bibliographic record
Abstract
Most of fine-needle aspiration (FNA) biopsies for follicular tumors of the thyroid are deemed 'indeterminate' or 'suspicious' with regard to malignancy, even though most of these lesions are benign. Therefore, additional diagnostic markers of malignancy are needed. Telomerase activity is present in most malignant tumors. Expression of the gene encoding human telomerase reverse transcriptase (hTERT) is very closely associated with telomerase activity, this gene is overexpressed in most thyroid carcinomas. We examined telomerase activity by the telomeric repeat amplification protocol (TRAP) and hTERT gene expression by in situ hybridization (ISH) in thyroid tissue including 6 follicular carcinomas and 15 follicular adenomas. ISH for hTERT gene was performed using FNA samples from the same patients. Telomerase activity was detected in all six of the follicular carcinomas and in five (33%) of the 15 follicular adenoma tissue specimens. hTERT gene expression was detected the follicular cancer cells in all of the lesions and in one (7%) of the 15 thyroid adenomas. Moreover, we demonstrated that hTERT gene expression was occurring in four (67%) of the 6 follicular carcinoma biopsy specimens obtained using FNA. These results suggest that the detection of hTERT gene expression using ISH tissue specimens can be used to distinguish between benign and malignant follicular lesions of the thyroid, however the detection of hTERT gene in FNA samples using ISH cannot be used to definitively diagnose follicular tumors of the thyroid.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".