Discrepancies in Vessel Diameter Measurements Between CTA and DSA in MCA M1 Occlusions: An Interobserver Study
Bibliographic record
Abstract
Purpose: Accurate vessel measurement is essential in endovascular thrombectomy (EVT) for acute ischemic stroke. Discrepancies between computed tomography angiography (CTA) and digital subtraction angiography (DSA) may impact procedural planning and device selection. This study compares vessel diameter measurements from CTA and DSA in patients with middle cerebral artery (MCA) M1 occlusions. Methods: In this single-center retrospective study, 90 consecutive patients who underwent EVT for MCA M1 occlusions between February 2020 and March 2024 were included. Vessel diameters were independently measured by 3 neuroradiologists using CTA and DSA (pre- and post-intervention). Statistical analysis included Wilcoxon signed-rank tests, intraclass correlation coefficient (ICC), and Bland–Altman analysis. Results: CTA consistently overestimated vessel diameter compared to DSA. The mean M1 diameter was 2.29 ± 0.27 mm on CTA and 2.16 ± 0.30 mm on pre-EVT DSA ( P < .001), with a median difference of 0.4 mm (IQR: 0.2-0.6 mm). In 70% of cases, CTA values exceeded DSA. Bland–Altman analysis confirmed a mean difference of +0.13 mm (limits of agreement: −0.25 to +0.51 mm). No significant change was observed between pre- and post-EVT DSA measurements ( P = .103). Clot-side M1 segments were significantly smaller than contralateral measurements on CTA ( P = .003). Inter-rater agreement was good (ICC = .785). Conclusions: CTA overestimates MCA M1 diameter relative to DSA. While the discrepancy is modest, it may influence device selection in borderline cases. Awareness of this variability is important, and further research is warranted to explore its clinical implications.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".