Utility of FDG PET and Cardiac MRI in Diagnosis and Monitoring of Immunosuppressive Treatment in Cardiac Sarcoidosis
Bibliographic record
Abstract
Purpose To compare the contributions of cardiac MRI and PET in the diagnosis and management of cardiac sarcoidosis (CS), with particular reference to quantitative measures. Materials and Methods This is a retrospective, observational study of 31 patients (mean age, 45.7 years) with proven extracardiac sarcoidosis and possible CS who were investigated with fluorine 18 fluorodeoxyglucose (FDG) PET/CT and cardiac MRI. Patients were treated at physicians’ discretion with repeat combined imaging after an interval of 102–770 days (median, 228 days). Results Significant myocardial FDG uptake was shown on visit 1 (myocardial maximum standardized uptake value [SUVmax] > 3.6) in 17 of 22 patients who were subsequently treated. Myocardial SUVmax decreased at follow-up (6.5 to 4.0; P < .01) and was matched by significant decreases in FDG-avid lung and mediastinal node disease. A volumetric measure of myocardium above a threshold SUV (cardiac metabolic volume) decreased from a mean of 42.5 to a mean of 4.1 (P < .001). This was associated with significant improvement in the left ventricular ejection fraction (LVEF) (45.8 increasing to 50.9; P < .031). There was no change in volume of late gadolinium enhancement at treatment. Patients who were untreated showed no change in any FDG PET or cardiac MRI parameter. Conclusion Myocardial FDG uptake in patients suspected of having CS is presumed to represent active inflammation. When treated with corticosteroids, this resolved or regressed at follow-up, with an improvement in LVEF and FDG-avid thoracic disease. Patients who were untreated showed no change in any parameter. Quantification of FDG-avid myocardium using cardiac metabolic volume is proposed as a useful objective measure for assessing response to therapy. Keywords: Adults, Cardiac, Cardiomyopathies, MR-Imaging, PET/CT © RSNA, 2020 See also commentary by Gutberlet in this issue.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".