1037 Non-infectious sarcoid-like inflammatory granulomatous conditions (NSIGC) associated with Immune checkpoint inhibitors (ICIs) for cancer: updated results from the international ICARUS consortium
Bibliographic record
Abstract
Background ICIs can cause a wide range of toxicities; however, limited data exists on NSIGC secondary to ICIs. Herein, we assembled the first international cohort of patients with cancer who developed NSIGC following ICI therapy.Methods We retrospectively collected data from 19 institutions worldwide on patients with cancer who received ICIs (alone or in combination with other drugs) between 2015-2025 and subsequently developed biopsy-confirmed NSIGC. The chi-squared goodness of fit test was used to analyze the association between different ICI regimens and NSIGC.Results The study included 141 patients with biopsy-confirmed NSIGC post-ICI. Of these, 58.9% (n=83) were male and 83.7% (n=118) were Caucasians. Median age at cancer diagnosis was 61 years. The top three cancers in the cohort were melanoma (51.8%; n=73), non-small cell lung cancer (16.3%; n=23), and renal cell carcinoma (6.4%; n=9). Our result showed a significant difference between expected and observed NSIGC frequencies across different ICI regimens (X2= 83.043, df=5, p < 0.0001)( table 1).Median time to NSIGC diagnosis post-ICI initiation was 7 months (range: 3.9-21.7 months). Of 141 patients, 53.9% (n=76) were diagnosed after treatment completion. Among these 76 patients, 59.4% (n=45) were diagnosed within 6 months, 14.5% (n=11) between 6-12 months, 9.2% (n=7) between 1-2 years, and 17.1% (n=13) were diagnosed after 2 years of treatment completion. The remaining 46.1% (n=65) were diagnosed during treatment. Among these, 41.5% (n=27) required permanent treatment discontinuation due to NSIGC and 4.6% (n=3) were re-challenged. Additionally, 40.4% (n=57) discontinued ICI therapy for other reasons, most commonly toxicity (47.4%; n=27) and disease progression (35.1%; n=20).There were 111 other immune-related adverse events reported in 80 patients, with colitis/diarrheas (n=21) and arthritis (n=19) being the most common. NSIGC was symptomatic in 28.4% (n=40), with 80% (n=32) achieving symptom resolution. The most commonly involved systems were skin/subcutaneous tissue (57.5%; n=23), followed by pulmonary system (32.5%; n=23). Steroid treatment for NSIGC was administered in 19.9% (n=28), with a median duration of 1.9 months. The overall response rate for the study population was 64.4%, and disease control rate was 77.3%.Conclusions To the best of our knowledge, this is the largest dataset to date demonstrating NSIGC as a rare side effect of ICIs. NSIGC frequently occurs after therapy completion but can also lead to ICI discontinuation. Biopsy confirmation is critical to prevent misdiagnosis, and further research is required to elucidate biology, risk factors, and implications for ICI continuation or rechallenge to optimize patient outcomes.Ethics Approval The IRB has reviewed the research study and determined that it meets the criteria for exemption from IRB review due to its retrospective design and minimal or no risk to patients. Name of the IRB: The University of Oklahoma Institutional Review Board for the Protection of Human Subjects ID: 17498 This study meets the criteria for a waiver of informed consent and is approved to be conducted without obtaining consent.Abstract 1037 Table 1NSIGC frequencies across different ICI regimens
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".