Hürthle Cell Tumors
Bibliographic record
Abstract
BACKGROUND: Since ret/PTC gene rearrangements are specific to papillary thyroid carcinoma (PTC), the diagnosis of Hürthle cell PTC (HCPTC) has recently been expanded to include a subset of Hürthle cell tumors (HCTs) that may lack both papillary architecture and/or classic nuclear features but that harbor a ret/PTC gene rearrangement. We hypothesize that such HCPTCs behave in a fashion analogous to other papillary carcinomas, while Hürthle cell carcinomas (HCCs) behave similarly to follicular carcinomas. EDUCATIONAL OBJECTIVES: At the conclusion of this article, participants should be able to discuss HCTs and to identify HCPTCS using molecular techniques. METHODS: A retrospective chart review was carried out on 56 patients with HCTs. All pathological specimens were analyzed for ret/PTC gene rearrangements. Hürthle cell adenoma (HCA) was defined as an HCT that did not exhibit capsular and/or vascular invasion and that lacked a ret/PTC gene rearrangement when evaluated by immunohistochemical and reverse transcription polymerase chain reaction analysis. An HCC was defined as an HCT with capsular and/or vascular invasion that lacked a ret/PTC gene rearrangement, and an HCPTC was defined as any HCT that harbored a ret/PTC gene rearrangement. RESULTS: The subclassification of the 56 HCTs was as follows: 21 HCAs, 15 HCCs, and 20 HCPTCs. No patients with HCA or HCC were ret/PTC positive. Five of the 6 patients with definite lymph node metastasis were in the HCPTC group, demonstrating that molecular analysis helps to explain biological behavior. CONCLUSIONS: Hürthle cell neoplasms can now be classified using histopathological as well as molecular criteria. It appears that the new subclassification of malignant HCTs into follicular (HCC) and papillary (HCPTC) variants identifies 2 distinct biological groups.
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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 | 0.000 | 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) | 0.005 | 0.001 |
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".