The role of 18F-FDG-PET/CT in initial staging and re-staging of head and neck cancer
Bibliographic record
Abstract
The aim of this study is to have a solid basis for the effectiveness of the 18F-FDG-PET/CT imaging technique, which hasknown advantages for patients with head and neck cancers during staging and restaging prior to treatment and to comparethis method with the corresponding clinical and radiological methods. A total of 139 patients with squamous cell head and neck carcinoma underwent PET/CT imaging. A total of 146 PET/CTimaging was performed in all patients. PET/CT imaging performed for staging and restaging in 36 and 103 patients,respectively. At least one conventional imaging (CI) as CT and/or MRI was performed for each one of the total patients.PET/CT studies revealed 66 true positive, 72 true negative, 4 false positive and 4 false negative results whereas the samevalues for CI were 65, 64, 4 and 6, respectively. When all studies were analyzed on the basis of lesion for PET/CT, specificity was 94.7%, and sensitivity being 94.2%,where as corresponding values for conventional imaging methods were found 94.1% and 91.5% respectively. Recurrentlesions have been detected with PET/CT and treatment management was changed in 29 of 139 patients.FDG-PET/CT improves the diagnostic accuracy in head and neck cancer patients.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".