Durability of Complete Response to Intralesional Interleukin-2 for In-Transit Melanoma
Bibliographic record
Abstract
BACKGROUND: Intralesional injection of interleukin-2 (IL-2) for in-transit melanoma (ITM) is associated with a high rate of complete response. However, there is a paucity of data on treatment durability and long-term outcomes. OBJECTIVES: To provide long-term data on patients with a complete response to IL-2 therapy for ITM. METHODS: Consecutive patients with ITM, treated with intralesional IL-2 therapy, at the Tom Baker Cancer Center were identified from April 2009 to August 2019. All patients received at least 4 cycles (every 2 weeks) of IL-2 (5 MIU/mL). Complete response was defined as sustained (ie, 3 months) clinical complete remission of all known in-transit disease. RESULTS: Sixty-five patients were treated with curative intent for in-transit disease with intralesional IL-2. Complete clinical response was identified in 44.6% (29/65). In this subset of patients, the median number of lesions per patient was 9 (range 1-40). The median total dose of IL-2 was 0.8 mL (IQR 0.4-1.5) per lesion. One patient received isolated limb infusion and 13.8% (4/29) received systemic immunotherapy as part of their initial management. At a median follow-up of 27 months (IQR 16-59), 34.5% (10/29) developed recurrent disease. Of these patients, 50.0% (5/10) presented with synchronous in-transit and distant metastases. The median time to recurrence was 10.5 months (IQR 5.8-16.3). CONCLUSION: With long-term follow-up, 65.5% of complete responders have a durable response to intralesional IL-2 therapy. In this cohort of patients, local in-transit recurrence is most likely to occur within 12 months and is often associated with concomitant distant disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".