Contrast-Enhanced MR Imaging in Acute Ischemic Stroke: T2* Measures of Blood-Brain Barrier Permeability and Their Relationship to T1 Estimates and Hemorrhagic Transformation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: rtPA is an effective treatment for AIS, yet it is substantially underused due to the increased risk of HT. Recent work suggests that permeability-related information can be extracted from routine T2*-based perfusion images by measuring the rR of the contrast agent. Given that other T2*-based measures have recently been proposed, the purpose of this study was to evaluate 4 such permeability measures in identifying patients with AIS who will proceed to HT. MATERIALS AND METHODS: Eighteen patients with AIS were examined within a mean of 3.3 +/- 1.4 hours postonset. Dynamic T2*-weighted imaging consisted of a single-shot EPI following a bolus of gadodiamide. HT was determined on follow-up CT or MR imaging at 24-72 hours. Mean values of rR, Peak Height, Recovery, as well as Slope were calculated and analyzed on the basis of follow-up HT status. RESULTS: Eight patients proceeded to HT. The mean rR for patients with HT was significantly greater than that for patients without HT (0.22 +/- 0.06 versus 0.14 +/- 0.06, P = .006), while there was a trend toward decreased %Recovery in patients with HT (76 +/- 6 versus 82 +/- 11%, P = .092). There was a significant negative correlation between %Recovery and rR (r = -0.88, P < .001). No significant differences or trends were detected with respect to Peak Height or Slope. CONCLUSIONS: Both rR and %Recovery can be readily extracted from a routine perfusion MR imaging dataset and show potential for identifying HT during the acute phase poststroke.
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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 | 0.003 |
| 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".