Adverse Effects of Immune-Checkpoint Inhibitors: A Comprehensive Imaging-Oriented Review
Bibliographic record
Abstract
Immune-checkpoint inhibitors (ICIs) are immunomodulatory monoclonal antibodies, which increase antitumor immunity of the host and facilitate T-cell-mediated actions against tumors. These medications have been used in recent years as a weapon against advanced stage malignancies, such as melanoma, renal cell carcinoma, lymphoma, small or non-small cell lung cancer, and colorectal cancer. Unfortunately, they are not free from possible adverse effects (immune-related adverse events-irAEs) that mainly affect skin, gastrointestinal, hepatic, and endocrine systems. Early diagnosis of irAEs is essential to correctly and rapidly manage patients, with ICIs suspension and therapies administration. Deep knowledge of the imaging and clinical patterns of irAEs is the key to promptly rule out other diagnoses. Here, we performed a review of the radiological signs and differential diagnosis, based on the organ involved. The aim of this review is to provide guidance to recognize the most significant radiological findings of the main irAEs, based on incidence, severity, and the role of imaging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".