Analysis of ret/PTC Gene Rearrangements Refines the Fine Needle Aspiration Diagnosis of Thyroid Cancer
Bibliographic record
Abstract
Papillary carcinoma (PC) represents the most common malignancy of the thyroid gland. Therefore, the assessment of fine needle aspiration biopsies of thyroid nodules rests heavily on the identification of nuclear features of PC. The ret/PTC oncogene, formed by several gene rearrangements, is specific for PC among thyroid tumors. In this study we examined thyroid aspirates for the presence of ret/PTC gene rearrangements by RT-PCR and Southern hybridization. We prospectively collected thyroid aspirates in Cytolyt solution and prepared slides for cytological examination using the ThinPrep method. All remaining material was then used for nucleic acid extraction with subsequent RT-PCR for the housekeeping gene PGK-1 to ensure ribonucleic acid integrity, for thyroglobulin to ensure the presence of follicular epithelial cells, and for the three most common ret/PTC gene rearrangements (ret/PTC-1, -2, and -3). The results of the first 73 cases with surgical follow-up were correlated with the cytological diagnosis and final histopathology. ret/PTC gene rearrangements were detected in 17 of 33 samples (52%) that were PC on histopathology; the presence of gene rearrangements was confirmed by molecular analysis of corresponding surgically resected frozen tissue. There were no false positives. The identification of ret/PTC gene rearrangements refined the diagnosis of PC in 9 of 15 specimens (60%) that would otherwise have been considered indeterminate and in 2 of 6 that were considered insufficient for cytological diagnosis. The results indicate that RT-PCR for ret/PTC is a specific marker that can be applied to fine needle aspiration biopsies and improves the diagnosis of malignancy when used as an adjunct to traditional cytology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".