The prognostic significance of non‐sentinel lymph node metastasis in cutaneous and acral melanoma patients—A multicenter retrospective study
Bibliographic record
Abstract
BACKGROUND: Whether non-sentinel lymph node (SLN)-positive melanoma patients can benefit from completion lymph node dissection (CLND) is still unclear. The current study was performed to identify the prognostic role of non-SLN status in SLN-positive melanoma and to investigate the predictive factors of non-SLN metastasis in acral and cutaneous melanoma patients. METHODS: The records of 328 SLN-positive melanoma patients who underwent radical surgery at four cancer centers from September 2009 to August 2017 were reviewed. Clinicopathological data including age, gender, Clark level, Breslow index, ulceration, the number of positive SLNs, non-SLN status, and adjuvant therapy were included for survival analyses. Patients were followed up until death or June 30, 2019. Multivariable logistic regression modeling was performed to identify factors associated with non-SLN positivity. Log-rank analysis and Cox regression analysis were used to identify the prognostic factors for disease-free survival (DFS) and overall survival (OS). RESULTS: Among all enrolled patients, 220 (67.1%) had acral melanoma and 108 (32.9%) had cutaneous melanoma. The 5-year DFS and OS rate of the entire cohort was 31.5% and 54.1%, respectively. More than 1 positive SLNs were found in 123 (37.5%) patients. Positive non-SLNs were found in 99 (30.2%) patients. Patients with positive non-SLNs had significantly worse DFS and OS (log-rank P < 0.001). Non-SLN status (P = 0.003), number of positive SLNs (P = 0.016), and adjuvant therapy (P = 0.025) were independent prognostic factors for DFS, while non-SLN status (P = 0.002), the Breslow index (P = 0.027), Clark level (P = 0.006), ulceration (P = 0.004), number of positive SLNs (P = 0.001), and adjuvant therapy (P = 0.007) were independent prognostic factors for OS. The Breslow index (P = 0.020), Clark level (P = 0.012), and number of positive SLNs (P = 0.031) were independently related to positive non-SLNs and could be used to develop more personalized surgical strategy. CONCLUSIONS: Non-SLN-positive melanoma patients had worse DFS and OS even after immediate CLND than those with non-SLN-negative melanoma. The Breslow index, Clark level, and number of positive SLNs were independent predictive factors for non-SLN status.
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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 | 0.001 | 0.001 |
| 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".