Multifocal Nonmetastatic Radioactive Iodine Avidity on Whole Body Scan After Thyroidectomy for Thyroid Cancer
Bibliographic record
Abstract
Background/Objective: Non-metastatic radioactive iodine (RAI) uptake can complicate the interpretation of whole-body scan (WBS) for differentiated thyroid carcinoma (DTC) post-thyroidectomy. We present a patient with DTC whose follow-up WBS showed nonmetastatic multifocal avidity in skeletal tissue, an uncommonly reported site of RAI uptake. Case report: A 42-year-old woman underwent a right hemithyroidectomy, followed by completion thyroidectomy and RAI remnant ablation therapy, for a 4.8 cm thyroid tumor consistent with stage pT3aNxMx follicular thyroid cancer. Follow-up WBS showed intense activity in the thyroid bed, right breast, left medial subcortical acetabulum, and several vertebral bodies. Her biochemical and clinical findings were not suggestive of cancer recurrence. Further workup with SPECT/CT and MRI showed no focal vertebral lesions and identified the left femoral lesion as a benign peripheral nerve sheath. Diagnostic mammography and ultrasound showed no evidence of suspicious breast lesions. Neck ultrasound was clear with no suspicious masses or pathologic lymphadenopathy. She remained in remission on continued active surveillance. Discussion: Nonmetastatic RAI uptake on WBS has many causes, including functional sodium-iodide symporter expression in nonthyroidal tissues, radioiodine accumulation in tissues and bodily fluids, and benign tumors. False-positive uptake can decrease the utility of post-treatment WBS in low-risk patients. Careful clinical examination, biochemical and radiologic follow-up, and close active surveillance can help distinguish false-positive uptake from metastatic or recurrent disease. Conclusion: We describe an uncommon case of RAI uptake in skeletal tissues after thyroidectomy for DTC, and we outline the steps taken to rule out underlying metastases.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".