Validation of a scoring system to predict non‐sentinel lymph node metastasis in melanoma
Bibliographic record
Abstract
BACKGROUND: Sentinel lymph node biopsy (SLNB) has been widely accepted as the lymph node sampling procedure of choice for melanoma patients. Current standards of practice suggest completion lymph node dissection (CLND) for patients with a positive SLNB result. The rationale for SLNB+/-CLND is for staging and prognosis as well as local control and possibly survival improvement. CLND, however, entails significant morbidity. In addition, most patients (approximately 80%) will have no further melanoma metastases in non-sentinel nodes and these patients may not benefit from the additional dissection. We had previously developed a score (based on patient age and the total size of metastasis within the SLN) that predicted which SLN-positive patients would have a positive CLND. Utilization of this scoring system would spare a significant number of melanoma patients the risks associated with CLND. The purpose of this study was to validate this score using different melanoma populations. METHODS: A retrospective chart review of all patients that had undergone SLNB for melanoma at four different Canadian centers was undertaken. Data from the Calgary Foothills Medical Center, the Winnipeg Health Sciences Center, and the Toronto Sunnybrook Health Sciences Center from January 1999 to present was collected. In addition, we identified all patients from April 2007 to present at the Misericordia Hospital in Edmonton for this study. This patient information had not been utilized when we were developing this score. The collected variables included patient age, Breslow thickness, result of SLNB, total size of SLN metastasis, largest size of SLN metastasis, and results of CLND. Logistic regression was used to test the significance of a score system's correlation (based on cutoff age of 55 years and cutoff total SLN metastasis of 5 mm) with the CLND results. We also used logistic regression to test the correlation of cutoff values of total SLN metastasis with non-sentinel lymph node (NSLN) metastasis. RESULTS: Data were collected on 599 patients across the four centers. Breslow thickness significantly correlated with SLN metastasis. The risk score system (based on patient age and total SLN metastasis) was significantly predictive of the CLND result in SLNB-positive patients. However, the age became non-significant on multivariate analysis. Total SLN metastasis emerged as the variable that is most predictive of NSLN metastasis. Patients with total SLN metastasis less than 2 mm had a 3.6% risk of NSLN metastasis, those with SLN metastasis from 2-5 mm had a 12.5% risk of NSLN metastasis, whereas those with total SLN metastasis of 5 mm or greater had a 30% risk of NSLN metastasis. CONCLUSION: Using cutoff values of 2 and 5 mm for total SLN metastasis, prediction of NSLN metastasis can be made in melanoma patients. Patients with less than 2 mm of total SLN metastasis are unlikely (<3.67% likelihood) to harbor NSLN metastasis; these patients may not benefit from additional nodal dissection beyond SLNB.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".