Treatment of recurrent mucosal melanoma of the oral cavity with topical imiquimod and pembrolizumab achieves complete histopathologic remission
Bibliographic record
Abstract
Mucosal melanomas constitute a subtype of melanoma with less effective treatments than cutaneous melanomas. We present a case of oral mucosal melanoma that recurred despite multiple resections and adjuvant temozolomide. Treatment with topical imiquimod combined with pembrolizumab achieved remission. A 56-year-old woman presented with a pigmented mass on her left anterior hard palate. Biopsy revealed malignant melanoma. The patient had resection with neck dissection with 3 months of adjuvant temozolomide due to positive margins. Malignant melanoma involving the hard palate recurred 1 year later requiring additional resection. Two years later, two additional pigmented lesions were found; further resections were deferred due to expected morbidity. Following 6 weeks of topical imiquimod treatment, the lesions shrunk significantly. Adjuvant pembrolizumab was added and complete histopathologic remission was observed in 6 months. The patient remained in remission for 4 years before new melanoma in situ was diagnosed, requiring five additional months of imiquimod. As of April 2021, there is no clinical evidence of melanoma. There are limited reports of oral melanoma treated with topical imiquimod. Here, imiquimod administered in combination with pembrolizumab achieved complete pathologic response.
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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 | 0.000 |
| 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 | 0.001 |
| 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".