Prediction of Infarction and Reperfusion in Stroke by Flow- and Volume-Weighted Collateral Signal in MR Angiography
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In proximal anterior circulation occlusive strokes, collateral flow is essential for good outcome. Collateralized vessel intensity in TOF- and contrast-enhanced MRA is variable due to different acquisition methods. Our purpose was to quantify collateral supply by using flow-weighted signal in TOF-MRA and blood volume-weighted signal in contrast-enhanced MRA to determine each predictive contribution to tissue infarction and reperfusion. MATERIALS AND METHODS: Consecutively (2009-2013), 44 stroke patients with acute proximal anterior circulation occlusion met the inclusion criteria with TOF- and contrast-enhanced MRA and penumbral imaging. Collateralized vessels in the ischemic hemisphere were assessed by TOF- and contrast-enhanced MRA using 2 methods: 1) visual 3-point collateral scoring, and 2) collateral signal quantification by an arterial atlas-based collateral index. Collateral measures were tested by receiver operating characteristic curve and logistic regression against 2 imaging end points of tissue-outcome: final infarct volume and percentage of penumbra saved. RESULTS: Visual collateral scores on contrast-enhanced MRA but not TOF were significantly higher in patients with good outcome. Visual collateral scoring on contrast-enhanced MRA was the best rater-based discriminator for final infarct volume < 90 mL (area under the curve, 0.81; P < .01) and percentage of penumbra saved >50% (area under the curve, 0.67; P = .04). Atlas-based collateral index of contrast-enhanced MRA was the overall best independent discriminator for final infarct volume of <90 mL (area under the curve, 0.94; P < .01). Atlas-based collateral index combining the signal of TOF- and contrast-enhanced MRA was the overall best discriminator for effective reperfusion (percentage of penumbra saved >50%; area under the curve, 0.89; P < .001). CONCLUSIONS: Visual scoring of contrast-enhanced but not TOF-MRA is a reliable predictor of infarct outcome in stroke patients with proximal arterial occlusion. By atlas-based collateral assessment, TOF- and contrast-enhanced MRA both contain predictive signal information for penumbral reperfusion. This could improve risk stratification in further studies.
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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 | 0.004 |
| 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) | 0.001 | 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".