Selectively false-positive radionuclide scan in a patient with sarcoidosis and papillary thyroid cancer: A case report and review of the literature
Bibliographic record
Abstract
BACKGROUND: Radioiodine and Tc-99 m pertechnetate scans are routinely relied upon to detect metastasis in papillary thyroid cancer; false-positive scans are relatively rare. To our knowledge, no published reports exist of sarcoidosis causing such selectively false-positive scans. METHODS: We present a case of a 41-year-old woman with known metastatic papillary thyroid cancer (T1bN1aMx) in whom sarcoidosis-affected cervical and mediastinal lymph nodes demonstrated uptake of thyroid-targeting radionuclides. Only the minority of these nodes demonstrated radionuclide uptake, raising the suspicion of adjacent or coexisting sarcoid and metastatic involvement. Selective uptake of thyroid-targeted radionuclides by isolated sarcoidosis is, to our knowledge, a previously undocumented occurrence. RESULTS: Biopsies of uptake-negative mediastinal nodes revealed sarcoidosis. Pathology from a subsequent neck dissection excising uptake-positive cervical nodes also showed sarcoidosis, with no coinciding malignancy. CONCLUSIONS: We document a case of sarcoidosis causing a selectively false-positive thyroid scintigraphy scan. It is useful for clinicians to be aware of potential false-positives and deceptive patterns on radionuclide scans when managing patients with both well-differentiated thyroid cancer and a co-existing disease affecting the nodal basins draining the thyroid gland.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".