Immune checkpoint inhibitor-related myocarditis: an illustrative case series of applying the updated Cardiovascular Magnetic Resonance Lake Louise Criteria
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) have improved outcomes for many types of cancer. However, ICI therapies are associated with the development of myocarditis, an immune-mediated adverse event associated with a high mortality rate. Therefore, prompt diagnosis and early intervention are of outmost importance. There is limited data on the application of cardiovascular magnetic resonance (CMR)-based modified Lake Louise Criteria (mLLC) with the use of relaxometry techniques for the diagnosis of ICI myocarditis. CASE SUMMARY: Four cancer patients undergoing ICI treatment presented with various clinical symptoms and troponin elevation to emergency/ambulatory clinics within 10-21 days after ICI initiation. On the suspicion of possible ICI-related myocarditis all patients underwent CMR within a few days after admission. Applying mLLC including relaxometry techniques, all patients met 'non-ischaemic injury criteria', while 3/4 patients met 'oedema criteria'. In most patients, quantitative mapping revealed substantially increased T1 values, while T2 values were only mildly increased or normal. In two patients with follow-up, CMR demonstrated improvement in findings after immunosuppressive treatment. However, there was only limited agreement between the degree of high-sensitive troponin levels and T1/T2 levels. DISCUSSION: The application of mLLC with T1/T2 mapping appears useful in the CMR diagnosis of acute ICI myocarditis with non-ischaemic myocardial injury criteria being the most common finding. The sensitivity of native T1 appears higher than T2 mapping in the acute diagnosis as well as in the assessment of treatment response. As troponin elevations may persist for some time with ICI myocarditis, CMR may represent an alternate strategy to monitor treatment response.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".