Cardiac magnetic resonance imaging in patients with suspected myocarditis from immune checkpoint inhibitor therapy – A real-world observational study
Bibliographic record
Abstract
Background and aims: Cardiotoxicity from immune checkpoint inhibitor (ICI) therapy is a challenge in clinical practice, and the assessment of ICI-related myocarditis (ICI-M) is often complicated by a variable phenotype. Cardiac magnetic resonance imaging (CMR) is used frequently, but evidence is poor. Here, we aim to assess the role of CMR in the assessment of suspected ICI-M in a real-world clinical setting. Methods: All patients receiving CMR at our centre for suspected ICI-M between September 2019 and January 2024 were included and retrospectively analysed. CMR parameters were correlated with clinical, laboratory and echocardiographic parameters and stratified for presence of myocarditis as per final diagnosis. Results: A total of 55 patients who received CMR for suspected ICI-M were analysed, including 25 patients with ICI-M and 30 patients with non-myocarditis cardiotoxicity (non-M). The mean age (ICI-M versus (vs.) non-M) was 65.7 ± 13.6 vs. 67.3 ± 9.9 (p = 0.61) years, 32.0 % vs. 26.7 % (p = 0.67) were female, and 40.0 % vs. 26.7 % (p = 0.29) had pre-existing coronary heart disease. Cardiac biomarkers and echocardiographic data did not differ between the groups. In CMR analysis, presence of LGE was associated with ICI-M (56.0 % in ICI-M vs. 26.7 % in non-M, p = 0.03). Myocardial oedema was generally rare and not associated with ICI-M. Conclusion: In this real-life assessment of routine clinical practice, the diagnostic assessment of ICI-M is challenged by low sensitivity of common diagnostic measures, often requiring a multimodal approach. Presence of LGE in CMR is associated with ICI-M, but sensitivity and specificity are low. Prospective data to improve diagnostic criteria is needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".