Thyroid incidentalomas on 18FDG-PET/CT: A metabolico-pathological correlation
Bibliographic record
Abstract
Background Thyroid incidentaloma is defined as an unsuspected thyroid lesion found on imaging study or while performing a surgery non-related to the thyroid gland. Most recent scientific literature tends to demonstrate a detection rate of 0.1–4.3% for incidental findings of thyroid focal uptake identified by 18F-fluorodeoxyglugose Positron Emission Tomography with computed tomography (18FDG-PET/CT) initially prescribed for nonthyroid disease. From 10.3 to 80.0% of patients who underwent further evaluation are diagnosed with malignant lesions. Our first objective is to determine the risk of malignancy confined in thyroid incidentalomas(IT) detected on 18FDG-PET/CT in patients treated in a tertiary care center (Centre Hospitalier Universitaire de Sherbrooke). Second, we want to identify a cut-off value for SUVmax in order to distinguish benign from malignant IT. Third, we look for predictive criterion that can be outlined to help in their management. Methods We retrospectively reviewed 40 914 charts of patients who had a 18FDG-PET/CT done in a tertiary center from 2004 to 2014. For each patient where a thyroid incidentaloma has been identified, Maximum Standardized Uptake Value (SUVmax), ultrasound report, cytology and histopathological results as well as oncologic outcomes were compiled and analyzed. Results In this study, the incidence for thyroid incidentaloma detected with 18FDG-PET/CT is 0.74%. The rate of malignancy present in IT is 8.2% based on histopathological results. Of the patients who underwent surgery, thyroid malignancy was identified in 54.3% of them. Cytoponction showed a strong correlation with final histopathological results ( p = 0.009). Conclusion Thyroid incidentalomas detected with 18FDG-PET/CT are relatively infrequent, but the potential risk of malignancy remains elevated. Fine needle aspiration biopsy is the investigation of choice to rule out a malignant incidentaloma when there is no other element in the clinical portrait to preclude such additional work up.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".