Experience With Sentinel Lymph Node Biopsy for Eyelid and Conjunctival Malignancies at a Cancer Center
Bibliographic record
Abstract
PURPOSE: To describe one center's experience with sentinel lymph node (SLN) biopsy in patients with eyelid and conjunctival malignancies performed with a smaller volume of technetium than was initially used and a small incision directly overlying the sentinel node(s). METHODS: A noncomparative interventional case series of 13 patients with clinically negative regional lymph nodes who underwent SLN biopsy for eyelid or conjunctival malignancies at The University of Texas M. D. Anderson Cancer Center between May 2002 and July 2003. Preoperative lymphoscintigraphy was performed with an injection of 0.3 mCi of technetium Tc-99m sulfur colloid in a volume of 0.2 mL. Images were taken as soon as the first SLN was detected through the gamma camera. Intraoperative mapping was performed with the same volume and concentration of technetium Tc-99m sulfur colloid along with an injection of isosulfan blue dye. RESULTS: Five patients had conjunctival melanoma, 6 had sebaceous cell carcinoma of the eyelid, and 2 had eyelid melanoma. SLN(s) were identified in all patients. In 12 patients, more than 1 SLN was identified. During surgery, no SLNs were blue. One patient with conjunctival melanoma had an SLN that was positive on histologic examination. There were no ocular or extraocular complications from the procedure except for mild temporary weakness of the marginal mandibular branch of the facial nerve in 2 patients that resolved completely within 4 to 6 weeks and without any further intervention. None of the patients had permanent blue tattooing of the conjunctival surface or eyelid skin. CONCLUSIONS: Our experience suggests that lymphoscintigraphy and SLN biopsy with a small volume of technetium Tc-99m sulfur colloid and small incisions, even without the use of the blue dye, can identify SLNs in patients with conjunctival and eyelid malignancies, and can be performed safely.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".