<scp>PET</scp> ‐ <scp>CT</scp> Test Properties for <scp>HPV</scp> Positive Unknown Primary of the Neck: A <scp>FIND</scp> Trial Correlative Study
Bibliographic record
Abstract
BACKGROUND: Diagnostic test properties for PET-CT in the setting of carcinoma of unknown primary (CUP) of the head and neck have been previously reported in the setting of limited pathologic correlation resulting in biased reporting. With the advent of transoral robotic techniques such as lingual tonsillectomy, the ability to identify small volume primaries has improved. This study aims to investigate the diagnostic test properties of PET-CT for carcinomas of unknown primary (CUP) of the head and neck. METHODS: A correlative analysis from our previously published prospective FIND trial between 08/2017 and 12/2019 was performed. Patients with p16 positive cervical nodes and no primary (CUP) on clinical examination or axial imaging were included. PET-CT images were prospectively reported prior to diagnostic surgery. Diagnostic test properties, based on pathologic correlation, were calculated. RESULTS: A total of 22 patients were included (N1: 10 [45.5%]; N2: 10 [45.5%]; N3: 2 [9%]). Nineteen (86.3%) patients were male, with a mean age of 59.1 years (range 47-68). Seventeen patients (77.2%) had a confirmation of an oropharyngeal primary after diagnostic transoral surgery: 5 (22.6%) in the ipsilateral palatine tonsil, 9 (41%) in the ipsilateral base of tongue, 2 (9%) with bilateral palatine tonsils, and 1 (4.5%) with ipsilateral palatine tonsil and contralateral base of tongue primary. Focal FDG uptake was reported in 13 patients (59%): 3 (13.6%) were reported positive, 8 (36.4%) were suspicious, and 2 (9%) were asymmetric uptake. Of the PET-CT positive, suspicious lesions, and asymmetric, 3 (100%), 0 (%), 6 (75%) were true positive, respectively. When all FDG uptake (positive, suspicious, and asymmetric) was classified as positive, the sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and false negative rate (FNR) were 53%, 20%, 11%, 69%, and 47%, respectively. CONCLUSIONS: PET-CT has an important role in the diagnostic evaluation of carcinoma of unknown primary diagnosis. However, sensitivity and specificity rates may be lower than previously suggested. Treatment planning should be based on pathologic confirmation where possible and not solely on PET-CT findings.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".