Immune Checkpoint Inhibitor-Induced Hepatic Injury: A Clinicopathologic Review
Bibliographic record
Abstract
Although immune checkpoint inhibitors (ICIs) have been a revolutionary milestone in immuno-oncology, immune-related adverse events (irAEs) may occur due to enhanced T cell activation and immune dysregulation. The irAEs can occur as early as within days to reportedly as late as up to 26 weeks. They may affect any organ system in the body, most commonly the luminal gastrointestinal tract, liver, skin, endocrine system, and lungs. The mechanisms of irAEs are complex and have not been fully understood. A breach of self-tolerance, which leads to autoantigen reactivity due to the enhanced activation and infiltration of T cells or the production of autoantibodies, and a non-specific autoinflammatory mechanism have been proposed. Limited data is available on the clinical and pathologic features of ICI-induced liver injury. This review presents an overview of the clinical and common histopathologic features and patterns of ICI-induced liver injury, the differential diagnoses, and the clinical management. Available data suggest that the histopathologic findings of ICI-induced hepatic injury are often non-specific and overlap with other challenging differential diagnoses. Therefore, a good knowledge of the histopathologic spectrum of ICI-induced hepatic injury and their differential diagnoses combined with the serological test results, clinical correlation, and communication with the clinical team is necessary to make an accurate and timely diagnosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".