Immune checkpoint inhibitor-induced gastrointestinal and hepatic injury: pathologists’ perspective
Bibliographic record
Abstract
Immune checkpoint inhibitors (CPIs) are a relatively new class of 'miracle' dugs that have revolutionised the treatment and prognosis of some advanced-stage malignancies, and have increased the survival rates significantly. This class of drugs includes cytotoxic T lymphocyte antigen-4 inhibitors such as ipilimumab; programmed cell death protein-1 inhibitors such as nivolumab, pembrolizumab and avelumab; and programmed cell death protein ligand-1 inhibitors such as atezolizumab. These drugs stimulate the immune system by blocking the coinhibitory receptors on the T cells and lead to antitumoural response. However, a flip side of these novel drugs is immune-related adverse events (irAEs), secondary to immune-mediated process due to disrupted self-tolerance. The irAEs in the gastrointestinal (GI) tract/liver may result in diarrhoea, colitis or hepatitis. An accurate diagnosis of CPI-induced colitis and/or hepatitis is essential for optimal patient management. As we anticipate greater use of these drugs in the future given the significant clinical response, pathologists need to be aware of the spectrum of histological findings that may be encountered in GI and/or liver biopsies received from these patients, as well as differentiate them from its histopathological mimics. This present review discusses the clinical features, detailed histopathological features, management and the differential diagnosis of the luminal GI and hepatic irAEs that may be encountered secondary to CPI therapy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".