Histopathological patterns of melanoma metastases in sentinel lymph nodes
Bibliographic record
Abstract
AIMS: Sentinel lymph node biopsy (SLNB) is an important component in the staging and treatment of cutaneous melanoma (CM). The medical literature provides only limited information regarding melanoma sentinel lymph node (SLN) histology. This report details the specific histological patterns of melanoma metastases in sentinel lymph nodes (SLNs) and highlights some key factors in evaluating SLNs for melanoma. METHODS: From 281 SLNB cases between June 1998 and May 2002, 79 consecutive cases of SLN biopsies positive for metastases from CM were retrospectively reviewed. The important characteristics of the SLNs and the metastatic foci are described. RESULTS: The median size of positive SLNs was 17 mm (range, 5-38). SLNs had a median of two metastatic foci (range, 1-11), with the largest foci being a median of 1.1 mm in size (range, 0.05-24). S-100 and HMB-45 staining was positive in 100% and 92% of the detected metastatic foci, respectively. The metastatic melanoma cells were epithelioid, spindled, and mixed in 86%, 5%, and 9% of cases. Metastatic foci were most often (86%) found in the subcapsular region of the SLN. Benign naevic cells were found coexisting in 14% of positive SLNs. CONCLUSIONS: Staining for S100 is more sensitive than HMB-45 (100% v 92%), but HMB-45 staining helped to distinguish benign naevic cells from melanoma. The subcapsular region was crucial in SLN evaluation, because it contained the metastases in 86% of cases. Evaluation of the subcapsular space should not be compromised by cautery artefacts or incomplete excision of the SLN.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".