CT Perfusion ASPECTS in the Evaluation of Acute Ischemic Stroke: Thrombolytic Therapy Perspective
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Advances in the management of acute ischemic stroke and medical imaging are creating pressure to replace the rigid one-third middle cerebral artery (MCA) and non-contrast-enhanced CT (NCCT) Alberta Stroke Program Early CT Score (ASPECTS) thresholds used for the selection of patients eligible for intravenous thrombolytic therapy. The identification of potentially salvageable ischemic brain tissue lies at the core of this issue. In this study, the role of CT perfusion ASPECTS in the detection of reversible ischemia was analyzed. MATERIALS AND METHODS: We retrospectively reviewed the clinical and imaging data of 92 consecutive patients who received intravenous thrombolytic therapy for acute (duration <3 h) ischemic stroke. Most of the patients underwent admission multimodal CT, and all patients had follow-up NCCT at 24 h. ASPECTS was assigned to all modalities and correlated with clinical and imaging parameters. Receiver-operating characteristic curve analysis was performed to determine optimal thresholds for different parameters to predict clinical outcome. RESULTS: A perfusion defect could be detected in 50% of the patients. ASPECTS correlated inversely with the clinical outcome in the following order: follow-up NCCT > cerebral blood volume (CBV) > mean transit time (MTT) > admission NCCT. The follow-up NCCT and the CBV displayed a statistically significant difference from the admission NCCT, while the MTT did not reach statistical significance. The threshold that best differentiated between good and bad clinical outcome on admission was CBV ASPECTS ≥7. In patients with CT perfusion ASPECTS mismatch, MTT and CBV ASPECTS essentially provided the lower and upper limits for the follow-up NCCT ASPECTS, thus defining the spectrum of possible outcomes. Furthermore, CT perfusion ASPECTS mismatch strongly correlated (r = 0.83) with the mismatch between the tissue at risk and the final infarct, i.e. the amount of salvaged tissue. This finding suggests that the CT perfusion ASPECTS mismatch adequately identifies the amount of potentially salvageable ischemic brain tissue. CONCLUSIONS: Parameters derived from the use of CT perfusion ASPECTS can detect reversible ischemia and are correlated with clinical outcome.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".