Analysis of Recurrence Patterns in Acral Versus Nonacral Melanoma: Should Histologic Subtype Influence Treatment Guidelines?
Bibliographic record
Abstract
Current surgical treatment of primary melanoma is uniform for all histosubtypes, although certain types of melanoma, such as acral lentiginous melanoma (ALM), have a worse prognosis. No study has explored the effectiveness of standard melanoma treatment guidelines for managing ALM compared with nonacral melanoma (NAM). Study subjects were identified from a prospectively enrolled database of patients with primary melanoma at New York University. Patients with ALM were matched to those with NAM (1:3) by gender and melanoma stage, including substage (ALM, 61; NAM, 183). All patients received standard-of-care treatment. Recurrence and survival outcomes in both cohorts were compared. ALM histologic subtype was an independent negative predictor of recurrence-free survival (hazard ratio [HR], 2.24; P=.001) and melanoma-specific survival (HR, 2.58; P=.001) compared with NAM. Recurrence was significantly more common in patients with ALM than in those with NAM (49% vs 30%; P=.007). For tumors less than 2 mm in thickness, a significantly higher recurrence rate was seen with ALM versus NAM (P=.048). No significant difference was seen in recurrence for tumors greater than 2 mm (P=.12). Notably, the rate of locoregional recurrence was nearly double for ALM compared with NAM (P=.001). The data presented herein reveal a high rate of locoregional failure in ALM compared with NAM when controlling for AJCC stage. These results raise the question of whether ALM may require more aggressive surgical treatment than nonacral cutaneous melanomas of equal thickness, particularly in tumors less than 2 mm thick. Larger multicenter trials are necessary for further conclusions.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".