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Record W4360865254 · doi:10.1161/svin.122.000811

Validation of a Novel Multiphase CTA Perfusion Tool Compared to CTP in Patients With Suspected Acute Ischemic Stroke

2023· article· en· W4360865254 on OpenAlexaff
Faysal Benali, Jianhai Zhang, Najratun Nayem Pinky, Fouzi Bala, Ibrahim Alhabli, Rotem Golan, Luis A. Souto Maior Neto, Ibukun Elebute, Chris Duszynski, Wu Qiu, Bijoy K. Menon

Bibliographic record

VenueStroke Vascular and Interventional Neurology · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsCircle Cardiovascular ImagingUniversity of Calgary
Fundersnot available
KeywordsMedicinePerfusion scanningPerfusionCerebral blood flowComputed tomography angiographyRadiologyIschemiaAngiographyNuclear medicineStroke (engine)Contrast (vision)OcclusionArea under the curveArtificial intelligenceComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.013
GPT teacher head0.260
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations4
Published2023
Admission routes1
Has abstractyes

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