Nodal Metastases in Stage 3 Head and Neck Melanoma: Patterns of Metastases and Patterns of Failure
Bibliographic record
Abstract
OBJECTIVE: Stage 3 patients with clinically positive nodal metastasis are treated with therapeutic neck dissection and adjuvant systemic therapy. The aim of our study was to examined the predictability of pre-operative CT as a nodal drainage assessment tool. METHODS: Retrospective review of all patients with clinically positive head and neck cutaneous melanoma between 2010 and 2019. Clinical disease was diagnosed as radiological suspicious, biopsy-proven node. A pre-operative CT evaluation for nodal metastasis was compared to pathology report. RESULTS: A total of 53 patients were included. Forty patients (75.5%) were males with a mean age of 59 (SD 15.52). The majority of patients (26.4%) had an unknown primary site. The most common sites for primary were the cheek in eight patients (15.1%) followed by forehead (9.4%) and lateral neck (9.4%). Preoperative CT predicted nodal disease in 84.6% of cases. The primary region that mainly failed from the previously described clinical prediction was the upper anterior neck with 83.3% parotid involvement. A total of 10 patients (18.9%) were diagnosis with non-clinical nodes on pathology with a median non-clinical node of 1 (range 1-2). Of them, 9 (90%) were in the same clinical levels detected by CT. Pre-operative CT was associated with a neck level accuracy of 98.1%. CONCLUSION: Stage 3 head and neck melanoma with clinically positive nodal metastasis that are eligible for an adjuvant systemic treatment, may benefit from a highly selective neck dissection according to their pre-operative imaging studies. This should be further evaluated in a large-scale clinical trial. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:4292-4297, 2024.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".