Evaluation of Intra-Lesional Interleukin 2 for the Treatment of In-Transit Melanoma Disease: L’évaluation de l’interleukine-2 intralésionnelle pour traiter les mélanomes en transit
Bibliographic record
Abstract
BACKGROUND: Intra-lesional interleukin 2 (IL-2) therapy trials for the treatment of in-transit melanoma using different treatment protocols have been published reporting varied results. This study assesses the results of IL-2 therapy in our institution and to evaluate the reproducibility of our response rates when using the same treatment protocol as another Canadian centre. METHODS: A retrospective review was undertaken of patients with in-transit melanoma who were treated with intralesional IL-2 in a single institution from 2010 to 2016. Responses were evaluated using RECIST criteria. Demographic data, tumour characteristics, follow-up data, in-transit-free interval, and survival data were collected and analysed. RESULTS: Forty-nine patients were identified. Overall tumour response rate was 72%, including complete response in 23 patients (47%) and partial response in 12 patients (24%). Stable disease was observed in 4% of patients and progressive disease in 25%. The main side effects were minor discomfort with injections and auto-limited flu-like symptoms. The presence of tumour-infiltrating lymphocytes may be a predictor of better response. CONCLUSION: This study confirms prior experience with intra-lesional IL-2, demonstrating it to be an effective, safe, and well-tolerated therapy for in-transit melanoma. Tumour-infiltrating lymphocytes as a predictor of better response warrant further study.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".