Abstract 19661: Integrin Imaging for the Detection of Diffuse Myocardial Fibrosis in Patients with Hypertrophic Cardiomyopathy: Direct Comparison Between Single-Photon Emission Computer Tomography and Cardiovascular Magnetic Resonance The SCAR Study
Bibliographic record
Abstract
Background Hypertrophic cardiomyopathy (HCM) promotes diffuse myocardial collagen synthesis, disarray and hypertrophy. Cardiovascular magnetic resonance (CMR) imaging using late gadolinium enhancement (LGE) can detect focal fibrosis in HCM, although the injury process in HCM may be global and diffuse. αvβ3 is a vitronectin integrin receptor associated with fibroblasts and collagen synthesis. A 99 Technetium compound ( 99m Tc- NC100692 ) that binds with high affinity to αvβ3 has been developed. Thus, we hypothesize that 99m Tc- NC100692 could be used to detect focal and diffuse myocardial fibrosis in patients with HCM. Methods 5 patients (62±11 years, 4 male) with a diagnosis of HCM from echocardiography were prospectively recruited. Patients underwent SPECT 99m Tc- NC100692 and CMR LGE imaging. Using a 17-segment model, 99m Tc- NC100692 images were visually assessed for myocardial uptake using a 3-point scale. LGE images were assessed for the presence or absence of enhancement per segment. Results For the 5 patients, 85 segments were evaluated and all had some degree of 99m Tc- NC100692 uptake. Three patients had evidence for focal LGE in 9 segments. Of these 9 segments, 5 had matched high-grade 99m Tc- NC100692 uptake. One patient had strong apical 99m Tc- NC100692 uptake without any evidence of hypertrophy nor LGE. The remaining 79 segments had low-grade 99m Tc- NC100692 uptake with lack of evidence for LGE. Conclusion Diffuse low-grade myocardial uptake of 99m Tc- NC100692 in patients with HCM may be suggestive of diffuse fibrosis. 99m Tc-NC10069 may serve as a marker of myocellular disarray although further studies are required to determine the specificity of 99m Tc-NC100692.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".