A comparison of CT versus MR imaging in acute stroke using ASPECTS: Will the “new” replace the “old” as the preferred imaging modality?
Bibliographic record
Abstract
49 Background: CT is considered less sensitive and reliable than MRI for the detection of ischemia in hyperacute stroke. This study compares these two modalities. Methods: We included all ischemic stroke patients who had both CT < 6 hours and MRI < 7 hours from onset. A quantitative scoring system (ASPECTS) was applied to CT, MR FLAIR and DWI at baseline and at follow-up (<36h) by five independent observers (2 neuroradiologists and 3 stroke neurologists) with only knowledge of stroke symptom side. CT was also assessed for hyperdense MCA and sylvian fissure MCA “dot” signs. The analysis was derived from consensus agreement among individual observers. MRA was the “gold standard” for vascular occlusion. Results: 52 patients had CT and MRI acutely. The median NIHSS was 9. The mean onset to CT was 125 minutes and to MRI 241 minutes (P< 0.001). Baseline CT and DWI ASPECTS were highly correlated (r 0.75 P<0.001). Median baseline CT, FLAIR and DWI scores using ASPECTS were 9, 10, and 8.5 respectively. At baseline DWI ASPECTS was 0.5 points lower than CT, but CT ASPECTS was a significant 1 point lower than FLAIR (P<0.03 and <0.001 respectively, Wilcoxon signed rank). However there was no difference between baseline CT and DWI at predicting follow-up DWI ASPECTS (P=0.12 Wilcoxon signed rank). Intraclass correlation coefficients to assess agreement among observer scores at baseline were moderate to excellent for CT and DWI (0.72 95% CI 0.62–0.81 and 0.71 95% CI 0.62–0.81 respectively) but was poor for FLAIR (0.43 95% CI 0.29–0.57). Hyperdense MCA and MCA “dot” signs had sensitivity 0.57 and specificity 0.96, using MRA as the “gold standard” for MCA occlusion (χ 2 P<0.001). Interobserver agreement for hyperdense signs was in the moderate to good range (kappa 0.50–0.67). Conclusion: CT appears as good as DWI for assessing ischemic tissue in hyperacute stroke when evaluated by experienced CT readers using ASPECTS. We speculate this surprising finding could be explained by excessive DWI basal ganglia hypointensity, MR susceptibility artifact at skull base, and less conspicuous DWI abnormality than previously recognised in the first hours of ischemia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".