Head and neck incidentalomas on positron emission tomographic scanning: ignore or investigate?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Incidental head and neck abnormalities are increasingly detected with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). Incidental thyroid lesions on PET are described in many studies; however, no reports have definitively identified incidental findings in multiple head and neck sites. The aim of this study was to (1) review the related literature, (2) identify the incidence and significance of head and neck incidentalomas on PET/computed tomography (CT) scanning, and (3) attempt to establish management recommendations for head and neck PET incidentalomas. STUDY DESIGN: Retrospective study. SETTING: Tertiary care centre. METHODS: Head and neck incidentaloma cases from whole-body 18F-FDG PET/CT scans were reviewed based on specific inclusion criteria from January 2009 to January 2010 at the Jewish General Hospital. The patients had been scanned for known or suspected malignant lesions in non-head and neck sites. Patients with incidental head and neck abnormalities were identified. RESULTS: The scans of 38 of 1565 (2.43%) subjects who underwent FDG-PET scanning for known or suspected cancer demonstrated head and neck incidentalomas. In 8 of 38 cases (21.05%), malignancies were discovered in the incidentaloma lesion (5 thyroid, 2 parotid, and 1 cervical lymph node), and all were new primary malignancies. Five of the 8 (62.5%) demonstrated significantly high standard uptake value (SUV). CONCLUSION: Head and neck PET/CT incidentalomas are quite common. A significantly high SUV strongly suggests the presence of malignancy. Head and neck incidentalomas merit consultation and further evaluation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".