Intraoperative Primary Tumor Identification and Margin Assessment in Head and Neck Unknown Primary Tumors
Bibliographic record
Abstract
OBJECTIVE: Surgical management of the unknown primary head and neck squamous cell carcinoma (UP HNSCC) remains controversial due to challenging clinical diagnosis. This study compares positron emission tomography-computed tomography (PET-CT) findings with intraoperative identification of primary tumors and compares intraoperative frozen-section margins to final histopathology. In addition, adjuvant therapy indications are provided. STUDY DESIGN: Prospective cohort study. SETTING: Academic university hospital. SUBJECTS AND METHODS: Sixty-one patients with UP HNSCC were included. Patients received PET-CT, followed by oropharyngeal transoral laser microsurgery (TLM). Margins were assessed intraoperatively using frozen sections and afterward by final histopathology. Adjuvant treatment was based on final histopathology. RESULTS: The sensitivity of localizing the primary tumor with PET-CT was 50.9% with a specificity of 82.5%. The primary tumor was found intraoperatively on frozen sections in 82% (n = 50) of patients. Five more tumors were identified on final histopathology, leading to a total of 90% (n = 55). Of the 50 intraoperatively found tumors, 98% (n = 49) had negative margins on frozen sections, and 90% (n = 45) were truly negative on final histopathology. Eighteen patients (29.5%) avoided adjuvant treatment. CONCLUSION: PET-CT localized the primary tumor in fewer than half the cases. This protocol identified 90% of primary tumors. Intraoperative frozen-section margin assessment has shown potential with a specificity of 92% compared to final histopathology. As a result, adjuvant therapy was avoided in almost one-third of our patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".