Identification of Penumbra and Infarct in Acute Ischemic Stroke Using Computed Tomography Perfusion–Derived Blood Flow and Blood Volume Measurements
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We investigated whether computed tomography (CT) perfusion-derived cerebral blood flow (CBF) and cerebral blood volume (CBV) could be used to differentiate between penumbra and infarcted gray matter in a limited, exploratory sample of acute stroke patients. METHODS: Thirty patients underwent a noncontrast CT (NCCT), CT angiography (CTA), and CT perfusion (CTP) scan within 7 hours of stroke onset, NCCT and CTA at 24 hours, and NCCT at 5 to 7 days. Twenty-five patients met the criteria for inclusion and were subsequently divided into 2 groups: those with recanalization at 24 hours (n=16) and those without (n=9). Penumbra was operationally defined as tissue with an admission CBF <25 mL x 100 g(-1) x min(-1) that was not infarcted on the 5- to 7-day NCCT. Logistic regression was applied to differentiate between infarct and penumbra data points. RESULTS: For recanalized patients, CBF was significantly lower (P<0.05) for infarct (13.3+/-3.75 mL x 100 g(-1) x min(-1)) than penumbra (25.0+/-3.82 mL x 100 g(-1) x min(-1)). CBV in the penumbra (2.15+/-0.43 mL x 100 g(-1)) was significantly higher than contralateral (1.78+/-0.30 mL x 100 g(-1)) and infarcted tissue (1.12+/-0.37 mL x 100 g(-1)). Logistic regression using an interaction term (CBFxCBV) resulted in sensitivity, specificity, and accuracy of 97.0%, 97.2%, and 97.1%, respectively. The interaction term resulted in a significantly better (P<0.05) fit than CBF or CBV alone, suggesting that the CBV threshold for infarction varies with CBF. For patients without recanalization, CBF and CBV for infarcted regions were 15.1+/-5.67 mL x 100 g(-1) x min(-1) and 1.17+/-0.41 mL x 100 g(-1), respectively. CONCLUSIONS: We have shown in a limited sample of patients that CBF and CBV obtained from CTP can be sensitive and specific for infarction and should be investigated further in a prospective trial to assess their utility for differentiating between infarct and penumbra.
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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 | 0.000 | 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 | 0.001 | 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".