MRA versus DSA for the follow-up imaging of intracranial aneurysms treated using endovascular techniques: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Treated aneurysms must be followed over time to ensure durable occlusion, as more than 20% of endovascularly treated aneurysms recur. While digital subtraction angiography (DSA) remains the gold standard, magnetic resonance angiography (MRA) is attractive as a non-invasive follow-up technique. Two different MRA techniques have traditionally been used: time-of-flight (TOF) and contrast-enhanced (CE) MRA. We analysed data from studies comparing MRA techniques with DSA for the follow-up of aneurysms undergoing endovascular treatment. Subgroup analysis of stent-assisted coiling (SAC) and flow diversion (FD) techniques was completed. METHODS: Comprehensive searches using the Embase, PubMed, and Cochrane databases were performed and updated to November 2018. Pooled sensitivity and specificity were calculated using aneurysm occlusion status as defined by the Raymond-Roy occlusion grading scale. RESULTS: The literature search yielded 1579 unique titles. Forty-three studies were included. For TOF-MRA, sensitivity and specificity of all aneurysms undergoing endovascular therapy were 88% and 94%, respectively. For CE-MRA, the sensitivity and specificity were 88% and 96%, respectively. For SAC and FD techniques, sensitivity and specificity of TOF-MRA were 86% and 95%, respectively. CE-MRA had sensitivity and specificity of 90% and 92%. CONCLUSION: MRA is a reliable modality for the follow-up of aneurysms treated using endovascular techniques. While the data are limited, MRA techniques can also be used to reliably follow patients undergoing FD and SAC. However, clinical factors must be used to optimize follow-up regimens for individual patients.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".