Follow-up of intracranial aneurysms treated by flow diverter: comparison of three-dimensional time-of-flight MR angiography (3D-TOF-MRA) and contrast-enhanced MR angiography (CE-MRA) sequences with digital subtraction angiography as the gold standard
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Follow-up of intracranial aneurysms treated by flow diverter with MRI is complicated by imaging artifacts produced by these devices. This study compares the diagnostic accuracy of three-dimensional time-of-flight MR angiography (3D-TOF-MRA) and contrast-enhanced MRA (CE-MRA) at 3 T for the evaluation of aneurysm occlusion and parent artery patency after flow diversion treatment, with digital subtraction angiography (DSA) as the gold standard. MATERIALS AND METHODS: Patients treated with flow diverters between January 2009 and January 2013 followed by MRA at 3 T (3D-TOF-MRA and CE-MRA) and DSA within a 48 h period were included in a prospective single-center study. Aneurysm occlusion was assessed with full and simplified Montreal scales and parent artery patency with three-grade and two-grade scales. RESULTS: Twenty-two patients harboring 23 treated aneurysms were included. Interobserver agreement using simplified scales for occlusion (Montreal) and parent artery patency were higher for DSA (0.88 and 0.61) and CE-MRA (0.74 and 0.55) than for 3D-TOF-MRA (0.51 and 0.02). Intermodality agreement was higher for CE-MRA (0.88 and 0.32) than for 3D-TOF-MRA (0.59 and 0.11). CE-MRA yielded better accuracy than 3D-TOF-MRA for aneurysm remnant detection (sensitivity 83% vs 50%; specificity 100% vs 100%) and for the status of the parent artery (specificity 63% vs 32%; sensitivity 100% vs 100%). CONCLUSIONS: At 3 T, CE-MRA is superior to 3D-TOF-MRA for the evaluation of aneurysm occlusion and parent artery patency after flow diversion treatment. However, intraluminal evaluation remains difficult with MRA regardless of the sequence used.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".