Diagnostic accuracy of a clinical carotid plaque MR protocol using a neurovascular coil compared to a surface coil protocol
Bibliographic record
Abstract
BACKGROUND: Carotid plaque imaging with MRI is becoming more commonplace, but practical challenges exist in performing plaque imaging with surface coils. PURPOSE: To compare the diagnostic performance of a carotid plaque MRI protocol using a standard neurovascular coil (Neurovascular Coil Protocol) to a higher-resolution carotid plaque MRI using carotid surface coils (Surface Coil Protocol) in characterizing carotid plaque. STUDY TYPE: Prospective study comparing two MR techniques in plaque characterization. POPULATION: Thirty-eight consecutive carotid artery disease patients. FIELD STRENGTH/SEQUENCE: pre/postcontrast and precontrast 3D IR-FSPGR, and 2) a Surface Coil Protocol using standard multicontrast MRI sequences. ASSESSMENT: Plaque characteristics analyzed by two independent neuroradiologists included intraplaque hemorrhage (IPH), lipid-rich necrotic-core (LRNC), and thin/ruptured fibrous cap (TRFC). STATISTICAL TESTS: Diagnostic performance of the Neurovascular Coil Protocol was compared to the Surface Coil Protocol reference standard using receiver-operating curves. RESULTS: For IPH, sensitivity, specificity, and area under the curve (AUC) of the Neurovascular Coil Protocol were 91.1% (95% confidence interval [CI] = 78.8-97.5%), 87.0% (95% CI = 66.4-97.2%), and 0.92, respectively. For LRNC without IPH sensitivity, specificity, and AUC were 73.3% (95% CI = 44.9-92.2%), 85.7% (95% CI = 67.3-96.0%), and 0.84, respectively. For TRFC, sensitivity, specificity, and AUC were 35.3% (95% CI = 14.2-61.7%), 97.6% (95% CI = 87.4-99.9%), and 0.66 respectively. Interobserver agreement for IPH, LRNC, and TRFC using the Neurovascular Coil Protocol were k = 0.87 (95% CI = 0.75-0.99), k = 0.54 (95% CI = 0.29-0.80), and k = 0.41 (95% CI = 0.08-0.74), respectively. DATA CONCLUSION: Our Neurovascular Coil Protocol has high sensitivity, specificity, and accuracy in identifying IPH and LRNC but is limited in assessment of TRFC. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1264-1272.
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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 | 0.005 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".