Sentinel Lymph Node Biopsy in Cutaneous Melanoma: Analysis of 240 Consecutive Cases
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate practical rules for sentinel lymph node biopsy for melanoma and discuss the indications and outcomes of 240 patients. METHODS: A prospective, nonrandomized analysis was performed on 240 patients in a referral cancer center. The median patient age was 51 years, and the median Breslow thickness was 1.60 mm. Ulceration was found in 30.4 percent of the cases. The median follow-up was 27.81 months. The sentinel lymph node biopsy was performed in 240 patients with cutaneous melanoma thicker or equal to 1 mm. The operation was performed with preoperative lymphoscintigraphy and postoperative immunohistochemistry. A statistical analysis was performed comparing the need for a gamma probe in each location, the value of the experience, the need for immunohistochemistry, positivity compared with Breslow thickness, reasons for the success of the lymph node localization, and evolution. RESULTS: A total of 263 lymph node basins were identified (160 in the axilla, 86 in the inguinal region, and 17 in less common locations, including the popliteal, epitrochlear, and cervical regions). In every lymph node basin, the success of localization was directly related to use of the probe. The success rate for finding the sentinel lymph node increased year by year. Lymph node analysis disclosed positivity of 12.5 percent with hematoxylin and eosin staining and 17.5 percent with immunohistochemistry (excluding the sentinel lymph node not found disclosed 13.2 percent with hematoxylin and eosin and 18.5 percent with HMB45). Immunohistochemistry increased positivity by 40 percent. Positivity was directly related to Breslow thickness (p < 0.001). CONCLUSIONS: This study shows the importance of the gamma probe in all lymph node basins but mainly in the axilla and unusual basins, as well as the importance of experience and immunohistochemistry. As a new procedure, it was possible to recognize the pattern of recurrence in the follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 teacher head, 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".