Metastatic Neuroendocrine Tumor in the Thyroid: Report of an Incidental Diagnosis
Bibliographic record
Abstract
Secondary tumors of the thyroid are quite rare with an estimated prevalence of < 1%. Herein, we describe the incidental discovery of metastatic small cell lung cancer in a non-toxic multinodular goiter. A 57-year-old lady has had a non-toxic multinodular goiter for over 10 years. She has had two thyroid fine needle aspirations (FNAs) in the past, which were reported benign. As the goiter was increasing in size and causing compression, she underwent a total thyroidectomy. Pathology revealed multi-centric high grade carcinoma with small cell/neuroendocrine features with the largest focus being 1.1 cm. Calcitonin staining was negative. Further imaging revealed a 68 × 37 × 51 mm soft tissue mediastinal mass and an 18 × 10 mm left upper lobe pulmonary nodule. She was then initiated on chemotherapy and radiation therapy for extensive stage small cell cancer of the lung. After two cycles of chemotherapy, restaging chest computed tomography (CT) showed interval improvement in lung masses. This rare presentation of small cell lung cancer with multi-centric metastasis to the thyroid highlights the need to obtain nodule directed FNAs, as the two non-directed FNAs done prior to surgery were benign. It also points out to clinicians that rapidly growing goiters are a cause for concern, and should be evaluated, even if patients are not symptomatic. J Endocrinol Metab. 2017;7(6):190-193 doi: https://doi.org/10.14740/jem468w
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".