Validation of a Novel Multiphase CTA Perfusion Tool Compared to CTP in Patients With Suspected Acute Ischemic Stroke
Bibliographic record
Abstract
Background We recently developed a novel machine learning‐based algorithm using multiphase computed tomography angiography (mCTA) to generate perfusion maps of the brain, similar to computed tomography perfusion (CTP) (ie, multiphase CTA perfusion [mCTAp]). Here, we aim to validate the clinical utility of mCTAp in detection of brain ischemia and its side, extent, and location. Methods In this prospective multi‐reader‐multi‐case analysis, we included baseline images: mCTAp ( StrokeSENS ‐algorithm) and CTP (4D; GE Healthcare) from 121 randomly selected patients whose scans were not part of algorithm‐development. After excluding 2/121 scans because of poor image‐quality, 3 experienced radiologists read time to maximum, and relative cerebral blood flow‐maps generated by the test (mCTAp) and reference (CTP) modality. The 2 reading sessions were separated by 5 days although the reading order was randomized. Core laboratory imaging assessments – that used non contrast computed tomography, mCTA, and CTP – were considered as ground‐truth. A mixed‐effects statistical model with “reader” as random effects variable was used to calculate the area under the curve (with 95% CI), sensitivity, and specificity for both modalities (mCTAp/CTP) for ischemia detection, affected side, and occlusion location. The time required for interpretation and inter‐rater variability in assessments were compared across the 2 modalities. Results Area under the curves (95% CI) for detecting ischemia using mCTAp and CTP were 0.85 (95% CI, 0.8–0.9) and 0.84 (0.8–0.9) respectively ( P =0.43). Area under the curves for the affected side were 0.94 (0.92–0.97) and 0.96 (0.94–0.98) ( P =0.69), respectively; for detecting large vessel occlusion were 0.84 (0.8–0.9) and 0.86 (0.8–0.9), ( P =0.31), respectively; M2‐or‐distal occlusion were 0.79 (0.73–0.84) and 0.88 (0.83–0.92) ( P =0.22), respectively, for anterior cerebral artery‐occlusion 0.82 (0.66–0.98) and 0.93 (0.82–1.00) ( P =0.15), respectively, and for posterior cerebral artery‐occlusions 0.9 (0.8–1) and 0.99 (0.98–0.99) ( P =0.01), respectively. The median (interquartile range [IQR]) time for image interpretation was 62 seconds (IQR, 46–78) and 59 seconds (IQR, 42–69) for mCTAp and CTP, respectively, ( P =0.15). Fleiss` Kappa‐values for inter‐rater reliability in detecting ischemia were 0.5 and 0.8 for mCTAp and CTP, respectively. Conclusion mCTAp shows similar performance and interpretation times compared to CTP in assisting readers to detect brain ischemia, affected side, and occlusion location, but mainly as it relates to proximal vessel occlusions. The proposed tool still needs further refinement for distal vessel occlusions. Nonetheless, mCTAp is a promising tool as it allows for acquisition of brain perfusion maps with lower radiation exposure, acquisition time, and contrast dose compared with additional CTP.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".