Differentiated thyroid cancer and positron emission computed tomography: when, how and why?
Bibliographic record
Abstract
INTRODUCTION: Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become an indispensable tool in oncology, offering both metabolic and anatomical insights into tumor behavior. Most differentiated thyroid carcinomas (DTC) are indolent and therefore FDG PET/CT is not routinely incorporated into management. However, in biologically aggressive DTCs, FDG PET/CT plays a crucial role in detecting recurrence and metastases. AREAS COVERED: This narrative review with articles from the last 25 years from PubMed database, explores the evolving role of FDG PET/CT, focusing on its utility in recurrence detection, staging, and follow-up of radioactive iodine (RAI)-refractory cases. Current guidelines recommend FDG PET/CT primarily for high-risk patients with elevated thyroglobulin levels and negative RAI scans (TENIS syndrome). We also examine advancements in PET imaging, novel radiotracers and theragnostic approaches that enhance diagnostic accuracy and treatment monitoring. EXPERT OPINION: While FDG PET/CT has proven valuable in biologically aggressive DTC, its routine use remains limited by cost, accessibility, and concerns regarding radiation exposure in younger patients requiring repeated imaging studies. Future developments in molecular imaging, including novel tracers and artificial intelligence-driven analysis, are expected to refine its role, leading to more personalized and effective management, though economic and reimbursement challenges remain important considerations for broader adoption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".