Hürthle Cell Neoplasms of the Thyroid
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the sonographic features of Hürthle cell neoplasms (HCNs) of the thyroid. METHODS: We retrospectively analyzed the sonographic appearance of 15 histologically proven HCNs in 15 patients aged 16 to 70 years (mean age, 44 years). Sonographic features that were reviewed included the size and echogenicity of the tumors, the presence of cystic areas or calcifications, and detectable blood flow on color Doppler imaging. Correlation of sonographic findings with pathologic results was performed. RESULTS: The tumors ranged from 0.4 to 7 cm in diameter, but most were less than 3 cm in diameter. Four (27%) of the 15 tumors were homogeneously hypoechoic. Two tumors (13%) were predominantly hypoechoic with isoechoic areas to thyroid parenchyma. Two (13%) neoplasms were isoechoic to thyroid parenchyma. Four (27%) tumors were predominantly isoechoic, containing hypoechoic areas, and 3 (20%) tumors were hyperechoic. Three neoplasms contained cystic components. None of the tumors contained calcifications. One tumor was avascular on Doppler examination. One neoplasm showed only peripheral blood flow. Thirteen tumors showed internal vascularity, 7 of them with peripheral blood flow. Twelve HCNs were benign, and 3 were malignant on pathologic examination. CONCLUSIONS: Hürthle cell neoplasms show a spectrum of sonographic appearances from predominantly hypoechoic to hyperechoic lesions and from peripheral blood flow with no internal flow to extensively vascularized lesions. Pathologic criteria differentiating benign and malignant HCNs (absence or presence of a capsular breach, vascular or extrathyroidal tissue invasion, nodal involvement, and distant metastasis) are beyond the resolution of sonography and fine-needle aspiration biopsy and require removal of the entire lesion. This precludes diagnosis and characterization of HCNs by sonography.
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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) | 0.001 | 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".