Measurement of Carotid Stenosis on Computed Tomographic Angiography: Reliability Depends on Postprocessing Technique
Bibliographic record
Abstract
PURPOSE: We previously demonstrated the validity of axial source (AxS) image quantification of computed tomographic angiography (CTA) visualized carotid stenosis. There is concern that AxS images may not accurately measure stenosis in patients with obliquely orientated stenosis and that measurements on axial oblique (AxO) multiplanar reformats (MPR), maximum intensity projections (MIP) images, or Doppler ultrasound (DUS) are superior. We tested the performance of AxS images against AxO MPRs, MIPs, and DUS techniques for stenosis quantification. METHODS: A total of 120 consecutive patients with CTA and DUS detected carotid disease were enrolled; carotids with occlusion, near occlusion, or stenosis <40% were excluded. Proximal and distal carotid diameters and North American Symptomatic Carotid Endarterectomy Trial (NASCET) style ratios were measured independently by 2 neuroradiologists on AxS, AxO, and MIP images on separate occasions in a blinded protocol. Intra- and interobserver agreements were determined for all measurements. The performance of different image types to identify > or =70% stenosis was assessed against a NASCET-style reference standard. RESULTS: Intra- and interobserver reliabilities for stenosis measurements were higher for both AxS (interclass correlation coefficients [ICC], 0.87-0.93 and 0.84-0.89) and AxO images (ICCs, 0.82-0.89 and 0.86-0.92) than for MIPs (ICCs, 0.66-0.86 and 0.79-0.82), respectively. Intra- and interobserver agreements on the NASCET ratio tended to be lower than proximal stenosis measurements. AxS and AxO image proximal stenosis measurements most accurately distinguished patients with > or =70% stenosis (0.90), followed by DUS (0.83) and MIP images (0.76). CONCLUSIONS: A single AxS image stenosis measurement was highly reproducible and accurate in the estimation of carotid stenosis, which precluded the need for AxO MPRs.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".