Quantitative assessment of carotid plaque composition using multicontrast MRI and registered histology
Bibliographic record
Abstract
MRI is emerging as a promising modality for monitoring carotid atherosclerosis. Multiple MR contrast weightings are required for identification of plaque constituents. In this study, eight MR contrast weightings with proven potential for plaque characterization were used to image carotid endarterectomy specimens. A classification technique was developed to create a tissue-specific map by incorporating information from all MR contrast weightings. The classifier was validated by comparison with micro-CT (calcification only) and with matched histological slices registered to MR images using a nonlinear warping algorithm (other components). A pathologist who was blinded to the classifier results manually segmented digitized histological images. The sensitivity of the classifier, as determined by pixel-by-pixel comparison with the pathologist's segmentation and micro-CT, was 60.4% for fibrous tissue, 83.9% for necrosis, 97.6% for calcification, and 65.2% for loose connective tissue. The corresponding values for specificity were 87.9%, 75.0%, 98.3%, and 94.9%, respectively. In conclusion, multicontrast MRI was successfully used in conjunction with a supervised classification algorithm to identify plaque components in endarterectomy specimens. Furthermore, this methodology will provide a framework for comparing different classification algorithms, and determining which combination of MR contrasts will be most valuable for in vivo plaque imaging.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".