Distinctive patterns on CT angiography characterize acute internal carotid artery occlusion subtypes
Bibliographic record
Abstract
Noninvasive computed tomography angiography (CTA) is widely used in acute ischemic stroke, even for diagnosing various internal carotid artery (ICA) occlusion sites, which often need cerebral digital subtraction angiography (DSA) confirmation. We evaluated whether clinical outcomes vary depending on the DSA-based occlusion sites and explored correlating features on baseline CTA that predict DSA-based occlusion site.We analyzed consecutive patients with acute ICA occlusion who underwent DSA and CTA. Occlusion site was classified into cervical, cavernous, petrous, and carotid terminus segments by DSA confirmation. Clinical and radiological features associated with poor outcome at 3 months (3-6 of modified Rankin scale) were analyzed. Baseline CTA findings were categorized according to carotid occlusive shape (stump, spearhead, and streak), presence of cervical calcification, Willisian occlusive patterns (T-type, L-type, and I-type), and status of leptomeningeal collaterals (LMC).We identified 49 patients with occlusions in the cervical (n = 17), cavernous (n = 22), and carotid terminus (n = 10) portions: initial NIH Stroke Scale (11.4 ± 4.2 vs 16.1 ± 3.7 vs 18.2 ± 5.1; P < 0.001), stroke volume (27.9 ± 29.6 vs 127.4 ± 112.6 vs 260.3 ± 151.8 mL; P < 0.001), and poor outcome (23.5 vs 77.3 vs 90.0%; P < 0.001). Cervical portion occlusion was characterized as rounded stump (82.4%) with calcification (52.9%) and fair LMC (94.1%); cavernous as spearhead occlusion (68.2%) with fair LMC (86.3%) and no calcification (95.5%); and terminus as streak-like occlusive pattern (60.0%) with poor LMC (60.0%), and no calcification (100%) on CTA.Our study indicates that acute ICA occlusion can be subtyped into cervical, cavernous, and terminus. Distinctive findings on initial CTA can help differentiate ICA-occlusion subtypes with specific characteristics.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".