Use of Myocardial T1 Mapping at 3.0 T to Differentiate Anderson-Fabry Disease from Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Purpose To compare left ventricular (LV) and right ventricular (RV) 3.0-T cardiac magnetic resonance (MR) imaging T1 values in Anderson-Fabry disease (AFD) and hypertrophic cardiomyopathy (HCM) and evaluate the diagnostic value of native T1 values beyond age, sex, and conventional imaging features. Materials and Methods For this prospective study, 30 patients with gene-positive AFD (37% male; mean age ± standard deviation, 45.0 years ± 14.1) and 30 patients with HCM (57% male; mean age, 49.3 years ± 13.5) were prospectively recruited between June 2016 and September 2017 to undergo cardiac MR imaging T1 mapping with a modified Look-Locker inversion recovery (MOLLI) acquisition scheme at 3.0 T (repetition time msec/echo time msec, 280/1.12; section thickness, 8 mm). LV and RV T1 values were evaluated. Statistical analysis included independent samples t test, receiver operating characteristic curve analysis, multivariable logistic regression, and likelihood ratio test. Results Septal LV, global LV, and RV native T1 values were significantly lower in AFD compared with those in HCM (1161 msec ± 47 vs 1296 msec ± 55, respectively [P < .001]; 1192 msec ± 52 vs 1268 msec ± 55 [P < .001]; and 1221 msec ± 54 vs 1271 msec ± 37 [P = .001], respectively). A septal LV native T1 cutoff point of 1220 msec or lower distinguished AFD from HCM with sensitivity of 97%, specificity of 93%, and accuracy of 95%. Septal LV native T1 values differentiated AFD from HCM after adjustment for age, sex, and conventional imaging features (odds ratio, 0.94; 95% confidence interval: 0.91, 0.98; P = < .001). In a nested logistic regression model with age, sex, and conventional imaging features, model fit was significantly improved by the addition of septal LV native T1 values (χ2 [df = 1] = 33.4; P < .001). Conclusion Cardiac MR imaging native T1 values at 3.0 T are significantly lower in patients with AFD compared with those with HCM and provide independent and incremental diagnostic value beyond age, sex, and conventional imaging features. © RSNA, 2018
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".