Long-Term Toxicities of Immune Checkpoint Inhibitor (ICI) in Melanoma Patients
Bibliographic record
Abstract
ICI therapy has greatly improved patient outcomes in melanoma, but at the cost of immune-related adverse events (irAEs). Data on the chronicity of irAEs, especially in real-world settings, are currently limited. We performed a retrospective chart review of 161 adult patients with melanoma treated with at least one cycle of ICI regimen in the adjuvant or metastatic setting: 129 patients received PD-1 inhibitor monotherapy and 32 received dual immunotherapy. Patients were grouped by duration of irAE: permanent (no complete resolution), long-term (resolution over a period ≥ 6 months), transient (resolution over a period < 6 months), or no irAEs. A total of 283 irAEs were reported in the whole patient population. Sixty-six (41.0%) patients developed permanent irAEs, fifteen (9.3%) experienced long-term irAEs as their longest-lasting toxicity, thirty-four (21.1%) developed transient irAEs only, and forty-six (28.6%) experienced no irAEs. Permanent irAEs occurred in 21 (65.6%) patients treated with dual immunotherapy and in 45 (34.9%) patients treated with monotherapy. The majority of permanent irAEs were endocrine-related (36.0%) or skin-related (32.4%). Grade 3-4 permanent irAEs occurred in 20 (12.4%) patients and included toxicities such as adrenal insufficiency, myocarditis, and myelitis. Fifty-three (32.9%) patients were still requiring treatment for long-term or permanent irAEs 6 months or more following the completion of ICI therapy, including twenty-four patients on thyroid hormone replacement and twenty-two on oral steroids. ICI treatment was temporarily interrupted for 64 (22.6%) irAEs and permanently discontinued due to irAEs in 38 patients (13.6% of irAEs, 23.6% of patients); additionally, 4 (2.5%) patients died of irAEs. Our findings show that ICI treatment in melanoma is associated with a wide range of toxicities that can be permanent and may have long-lasting impacts on patients, which should therefore be discussed when obtaining consent for treatment.
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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 | 0.001 | 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 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".