A head-to-head comparison of fast-SENC and feature tracking to LV long axis strain for assessment of myocardial deformation in chest pain patients
Bibliographic record
Abstract
BACKGROUND: Myocardial strain imaging has gained importance in cardiac magnetic resonance (CMR) imaging in recent years as an even more sensitive marker of early left ventricular dysfunction than left-ventricular ejection fraction (LVEF). fSENC (fast strain encoded imaging) and FT (feature tracking) both allow for reproducible assessment of myocardial strain. However, left-ventricular long axis strain (LVLAS) might enable an equally sensitive measurement of myocardial deformation as global longitudinal or circumferential strain in a more rapid and simple fashion. METHODS: In this study we compared the diagnostic performance of fSENC, FT and LVLAS for identification of cardiac pathology (ACS, cardiac-non-ACS) in patients presenting with chest pain (initial hscTnT 5-52 ng/l). Patients were prospectively recruited from the chest pain unit in Heidelberg. The CMR scan was performed within 1 h after patient presentation. Analysis of LVLAS was compared to the GLS and GCS as measured by fSENC and FT. RESULTS: In total 40 patients were recruited (ACS n = 6, cardiac-non-ACS n = 6, non-cardiac n = 28). LVLAS was comparable to fSENC for differentiation between healthy myocardium and myocardial dysfunction (GLS-fSENC AUC: 0.882; GCS-fSENC AUC: 0.899; LVLAS AUC: 0.771; GLS-FT AUC: 0.740; GCS-FT: 0.688), while FT-derived strain did not allow for differentiation between ACS and non-cardiac patients. There was significant variability between the three techniques. Intra- and inter-observer variability (OV) was excellent for fSENC and FT, while for LVLAS the agreement was lower and levels of variability higher (intra-OV: Pearson > 0.7, ICC > 0.8; inter-OV: Pearson > 0.65, ICC > 0.8; CoV > 25%). CONCLUSIONS: While reproducibility was excellent for both FT and fSENC, it was only fSENC and the LVLAS which allowed for significant identification of myocardial dysfunction, even before LVEF, and therefore might be used as rapid supporting parameters for assessment of left-ventricular function.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".