Necrotizing and Nonnecrotizing Granulomatous Reactions in Patients With Cancer Treated With Immune Checkpoint Inhibitors: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: Immune checkpoint inhibitors (ICI) have improved cancer outcomes but often cause immune-related adverse events, including granulomatous reactions (GRs). We analyzed GRs in patients receiving anticytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4), antiprogrammed cell death protein 1 (anti-PD1)/antiprogrammed death ligand 1 (anti-PDL1), and anti-CTLA4/anti-PD1 therapies. METHODS: We performed a literature review of GRs in patients receiving ICI. Data were extracted from 166 articles, including demographics, GR organ distribution, and pathological findings. RESULTS: In 261 patients, the mean age of GR onset was 59.3 (SD 13.0) years. The most common cancer types were melanoma (57%) and lung cancer (21%). Lymph nodes (52%) and skin (35%) were the predominantly affected organs; however, GRs also involved the liver, kidney, and bone. Granulomas were nonnecrotizing in 64% of cases and necrotizing in 15% of cases. Forty-five percent of patients were treated with systemic corticosteroids (CS), and 11% required a CS-sparing agent. Median follow-up time was 10.1 (IQR 4.0-22.2) months. Most GRs (64%) had resolved by last follow-up. Compared to those treated with combination ICI, patients treated with anti-PD1/anti-PDL1 monotherapy were older and had a longer time to onset of GR. They were less likely to be treated with CS for GRs. In patients with melanoma, necrotizing GRs were more common with combination ICI. GRs in the lungs and lymph nodes were more likely to be nonnecrotizing, and GRs in the liver were more commonly necrotizing. CONCLUSION: GRs in patients with cancer treated with ICI can occur in many organ systems and were most commonly nonnecrotizing. Patients treated with combination ICI had more severe reactions. Most GRs resolved with CS treatment or ICI discontinuation. (PROSPERO ID: CRD42024501205).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".