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Record W4386033160 · doi:10.1136/jnis-2023-esmint.5

O05/57  Validation of a novel multiphase CTA perfusion tool compared to CTP in patients with suspected acute ischemic stroke

2023· article· en· W4386033160 on OpenAlexaff
Faysal Benali, Jianhai Zhang, Najratun Nayem Pinky, Fouzi Bala, Ibrahim Alhabli, Rotem Golan, Souto Neto, Ibukun Elebute, Chris Duszynski, Wu Qiu, Bijoy K. Menon

Bibliographic record

VenueAbstracts · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsCircle Cardiovascular Imaging
Fundersnot available
KeywordsMedicinePerfusionPerfusion scanningRadiologyAngiographyStroke (engine)Nuclear medicineOcclusionInternal medicine

Abstract

fetched live from OpenAlex

<h3>Introduction</h3> A recently developed multiphase-computed-tomography-angiography(mCTA) tool generates perfusion maps, similar to CT-perfusion(CTP) (i.e., mCTA-perfusion[mCTAp]). <h3>Aim of Study</h3> To validate the clinical utility of mCTAp. <h3>Methods</h3> In this multi-reader-multi-case analysis, we included baseline images: mCTAp(<i>StrokeSENS</i>-algorithm) and CTP(4D; GE-Healthcare) from 121 randomly selected patients whose scans were not part of algorithm-development. After excluding 2/121 scans due to poor image-quality, three experienced radiologists read Tmax-and rCBF-maps generated by the test(mCTAp) and reference(CTP) modality. The two reading sessions were separated by 5-days with randomized reading order. Core-laboratory imaging assessments-that used NCCT, mCTA and CTP-were considered as ground-truth. We used ‘reader’ as a random-effect to calculate the diagnostic performance for both modalities(mCTAp/CTP) regarding ischemia detection and side/location. Interpretation-time and inter-rater variability were compared across the modalities. <h3>Results</h3> AUCs(95%CI) for detecting ischemia using mCTAp and CTP were 0.85(95%CI0.8–0.9) and 0.84(0.8–0.9) respectively(p=0.43). AUCs for the affected side were 0.94(0.92–0.97) and 0.96(0.94–0.98) (p=0.69) respectively; for detecting LVO 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 ACA-occlusion 0.82(0.66–0.98) and 0.93(0.82–1.00) (p=0.15) respectively and for PCA-occlusions 0.9(0.8–1) and 0.99(0.98–0.99) (p=0.01) respectively. The median(IQR) time for image interpretation was 62s(IQR 46–78) and 59s(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 compared to CTP in assisting readers to detect ischemia and its side/location, requiring less radiation exposure, acquisition time and contrast-dose compared to additional-CTP, but mainly as it relates to proximal vessel occlusions. <h3>Disclosure of Interest</h3> Dr. Menon holds patents on systems of triage in acute stroke, for LVO detection and for mCTAp, and stock ownership in Circle Cardiovascular Inc. Dr. Bala has nothing to declare. Dr. Duszynski is an employee of, and holds stock options for Circle Cardiovascular Imaging Inc. Dr. Nayem Pinky, Dr. Golan and Luis A Souto Maior Neto are employees of Circle Cardiovascular Imaging Inc. All other co-authors have nothing to disclose.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.286
Teacher spread0.273 · 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 teacher head, 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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Citations0
Published2023
Admission routes1
Has abstractyes

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