Validation of Automatically Classified Magnetic Resonance Images for Carotid Plaque Compositional Analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: MRI may be used for noninvasive assessment of atherosclerotic lesions; however, MRI evaluation of plaque composition requires validation against an accepted reference standard, such as the American Heart Association (AHA) lesion grade, defined by histopathological examination. METHODS: Forty-eight carotid endarterectomy specimen cross-sections had AHA lesion grade determined histopathologically and were concurrently imaged using combinations of 8 MRI contrast weightings in vitro. A maximum likelihood classification algorithm generated MRI "maps" of plaque components, and an AHA lesion grade was assigned correspondingly. Additional analyses compared classification accuracy obtained with a commonly used set of magnetic resonance contrast weightings [proton density (PDw), T1 (T1w), and partial T2 (T2w)] to accuracy obtained with the combination of PDw, T1w, and diffusion-weighted (Dw) contrast. RESULTS: For the 8-contrast combination, the sensitivities for fibrous tissue, necrotic core, calcification, and hemorrhage detection were 83%, 67%, 86%, and 77%, respectively. The corresponding specificities were 81%, 78%, 99%, and 97%. Good agreement (79%) between magnetic resonance and histopathology for AHA classification was achieved. For the PDw, T1w, and Dw combination, the overall classification accuracy was insignificantly different at 78%, whereas the overall classification accuracy using PDw, T1w, and partial T2w contrast weightings was significantly lower at 67%. CONCLUSIONS: This study provides proof-of-principle that the composition of atherosclerotic plaques determined by automated classification of high-resolution ex vivo MRI accurately reflects lesion composition defined by histopathological examination.
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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) | 0.000 | 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".