Inter- and Intraobserver Agreement of <sup>18</sup>F-FDG PET/CT Image Interpretation in Patients Referred for Assessment of Cardiac Sarcoidosis
Bibliographic record
Abstract
Recent studies have reported the usefulness of 18F-FDG PET in aiding with the diagnosis and management of patients with cardiac sarcoidosis (CS). However, image interpretation of 18F-FDG PET for CS is sometimes challenging. We sought to investigate the inter- and intraobserver agreement and explore factors that led to important discrepancies between readers. Methods: We studied consecutive patients with no significant coronary artery disease who were referred for assessment of CS. Two experienced readers masked to clinical information, imaging reports, independently reviewed 18F-FDG PET/CT images. 18F-FDG PET/CT images were interpreted according to a predefined standard operating procedure, with cardiac 18F-FDG uptake patterns categorized into 5 patterns: none, focal, focal on diffuse, diffuse, and isolated lateral wall or basal uptake. Overall image assessment was classified as either consistent with active CS or not. Results: One hundred scans were included from 71 patients. Of these, 46 underwent 18F-FDG PET/CT with a no-restriction diet (no-restriction group), and 54 underwent 18F-FDG PET/CT with a low-carbohydrate, high-fat and protein-permitted diet (low-carb group). There was agreement of the interpretation category in 74 of 100 scans. The κ-value of agreement among all 5 categories was 0.64, indicating moderate agreement. For overall clinical interpretation, there was agreement in 93 of 100 scans (κ = 0.85). When scans were divided into the preparation groups, there was a trend toward higher agreement in the low-carb group versus the no-restriction group (80% vs. 67%, P = 0.08). Regarding the overall clinical interpretation, there was also a trend toward greater agreement in the low-carb group versus the no-restriction group (96% vs. 89%, P = 0.08). Conclusion: The interobserver agreement of cardiac 18F-FDG uptake image patterns was moderate. However, agreement was better regarding overall interpretation of CS. Detailed prescan dietary preparation seemed to improve interobserver agreement.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".