Thyroid Cancer Presenting as a PET Incidentaloma in a Patient With Concomitant Breast Cancer Metastases to the Thyroid
Bibliographic record
Abstract
INTRODUCTION: Metastases to the thyroid gland are considered a rare cause of thyroid tumor. Furthermore, a relationship between breast and thyroid carcinoma has been previously proposed. CASE DESCRIPTION: We describe the case of a 59-year-old woman who presented with simultaneous papillary and breast carcinoma within the thyroid gland. F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) done for the evaluation of her metastatic breast cancer revealed a thyroid incidentaloma with a high metabolic rate (standardized uptake value [SUV] of 13). She underwent thyroidectomy and the pathology revealed papillary thyroid carcinoma corresponding to the lesion visualized on FDG PET. However, small metastatic implants of breast carcinoma were seen within the opposite thyroid lobe. CONCLUSION: This is a rare description of a concomitant papillary thyroid carcinoma presenting as an FDG PET incidentaloma alongside breast cancer metastases to the thyroid gland. Thyroid and breast cancer sometimes occur in the same patient. However, no explanation has been found to link these 2 cancers. Although uncommon, FDG PET thyroid incidentalomas seem to harbor a higher rate of malignancy than incidentalomas found on conventional imaging. In the appropriate clinical setting, it is therefore suggested to investigate these lesions thoroughly.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".