Immunotherapy‐Related Gastritis in Small Cell Lung Cancer Treatment With Durvalumab—A Case Report
Bibliographic record
Abstract
BACKGROUND: Durvalumab is a PD-L1 inhibitor that triggers a blockade resulting in enhanced anti-tumor responses related to increased T-cell activation. There is potential for numerous immune-related adverse events (irAEs) with this treatment, most of which have been shown to be effectively managed with high-dose steroids. Immunotherapy-related gastritis, while rare compared to other irAEs is an emerging concern as the use of immune checkpoint inhibitors (ICIs) increases. CASE: This case study examines a 66-year-old female with extensive-stage small cell lung cancer (ES-SCLC) treated with durvalumab, alongside chemotherapy. Twenty-three months into treatment, she developed non-specific gastrointestinal (GI) symptoms including abdominal pain, appetite loss, and significant weight loss. Despite conservative management, resolution only occurred following the use of high-dose steroids, a finding consistent with immunotherapy-related gastritis. The patient then went onto a successful rechallenge of immunotherapy. CONCLUSION: This case represents the first report on the rare occurrence of immune-related gastritis in an ES-SCLC patient who has been on immunotherapy for nearly 2 years. Current literature is limited in the understanding of underlying mechanisms of PD-L1-related irAEs and optimal management strategies for rare toxicities like gastritis in immunotherapy-treated cancer patients. This report aims to address the unmet need for further research on rare toxicities to immunotherapy in unique cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".