Abstract 131: Multiphase CT Angiography Perfusion Vs. CT Perfusion In Predicting Final Infarction In Acute Stroke Patients
Bibliographic record
Abstract
Background: In acute ischemic stroke (AIS), Computed Tomography Perfusion (CT, CTP) is the most widely used technique for determining extent of tissue likely to die even after successful reperfusion. However, CTP results in higher radiation dose to the patient, is affected by motion, and is not widely available. Multiphase CT Angiography (mCTA), by contrast, is a low radiation extension to the ubiquitous CT Angiography workflow. Here, we evaluate StrokeSENS mCTA Perfusion, a software tool that uses mCTA to estimate brain tissue perfusion, and compare it to CTP in its ability to predict final infarction. Methods: 551 subjects with baseline mCTA, Non-contrast CT (NCCT), and CTP were included. Of these, 480 were part of the development dataset used to derive the mCTA Perfusion algorithm while the remaining 71 were included in the test dataset. T max and CBF perfusion maps were generated on CTP using GE CTP-4D Perfusion, and on mCTA using StrokeSENS mCTA Perfusion. Final infarction was manually segmented on 24-48h MRI/NCCT by 2 experts using ITK-SNAP. Voxel values from CTP and mCTA were assessed in their ability to predict final infarction at an individual patient level (AUC calculated for each patient, then averaged across patients) and for the combined patient data (voxels pooled across patients, then one AUC calculated), then compared (two-sided difference test p-value, p). Results: At patient level, mCTA Perfusion T max AUC was 77.7% (95% c.i.: [74%, 82%]) while CTP T max AUC was 74.6% (95% c.i.: [71%, 79%]), p=0.15. mCTA Perfusion CBF AUC was 68.5% (95% c.i.: [65%, 72%]) while CTP CBF AUC was 69.8% (95% c.i.: [67%, 73%]), p=0.43. In combined patient data analysis, mCTA perfusion T max AUC was 84.13% while CTP T max AUC was 81.36%, p=0. mCTA perfusion CBF AUC was 74.44% while CTP CBF AUC was 72.51%, p=0. Conclusion: StrokeSENS mCTA Perfusion software is similar to traditional CT Perfusion in its ability to predict final infarction in patients with acute ischemic stroke.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".