Management of head and neck primary unknown squamous cell carcinoma using combined positron emission tomography‐computed tomography and transoral laser microsurgery
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The unknown primary of the neck is commonly encountered by the head and neck surgeon. Despite the exhaustive diagnostic tools employed in traditional detection protocols, many unknown primaries are not found, and the patient is subjected to wide-field radiation and chemotherapy during treatment. Localizing the primary tumor has demonstrated therapeutic benefits, improved quality of life, and overall survival. The authors' objective was to determine the efficacy of a new management protocol for unknown primaries of the head and neck. STUDY DESIGN: Prospective cohort study. METHODS: Our technique involved a preoperative positron emission tomography-computed tomography (PET-CT) followed by a planned transoral laser microsurgery (TLM) approach. Efficacy was assessed based on survival statistics, disease control, detection rates, the proportion of patients not receiving adjuvant therapy, and the proportion of PET-CT scans helpful for detection of the primary cancer. RESULTS: The occult primary was located in 25 of the 27 patients (93%), with the majority found in the palatine tonsil (52%). Both overall survival and disease-specific survival was 80% at 36 months. Local control was achieved in 100% of patients. After surgery, 37.0% (n = 10) received adjuvant radiation alone and 33.3% (n = 9) of patients went on to receive adjuvant chemoradiation. On imaging, 72% (n = 18) of PET-CT scans correctly localized the primary tumor. CONCLUSIONS: Occult head and neck primaries present a diagnostic challenge that is not adequately overcome using traditional detection protocols. The current study presents our unique protocol at Dalhousie University, which demonstrates the efficacy of the PET-CT TLM protocol from both a detection and therapeutic perspective. LEVEL OF EVIDENCE: 4. Laryngoscope, 128:2307-2311, 2018.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".