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Record W4281771455 · doi:10.1148/radiol.212283

<sup>18</sup>F-NaF PET/MRI for Detection of Carotid Atheroma in Acute Neurovascular Syndrome

2022· article· en· W4281771455 on OpenAlexaff
Jakub Kaczynski, Stephanie Sellers, Michael A. Seidman, Maaz Syed, Martin Dennis, Gillian Mcnaught, Maurits A. Jansen, Scott Semple, Carlos J. Alcaide‐Corral, Adriana Tavares, Tom MacGillivray, Samuel Debono, Rachael O. Forsythe, Andrew L. Tambyraja, Piotr J. Slomka, Jonathon Leipsic, Marc R. Dweck, William Whiteley, Joanna M. Wardlaw, Edwin J.R. van Beek, David E. Newby, Michelle C. Williams

Bibliographic record

VenueRadiology · 2022
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsArtificial Intelligence in Medicine (Canada)St. Paul's HospitalUniversity Health NetworkUniversity of British Columbia
FundersBiotechnology and Biological Sciences Research CouncilMedical Research CouncilUniversity of EdinburghBritish Heart FoundationWellcome Trust
KeywordsMedicineCulpritAmaurosis fugaxAtheromaStenosisNeurovascular bundleStroke (engine)RadiologyMagnetic resonance imagingInternal medicineProspective cohort studyCardiologyNuclear medicinePathologyMyocardial infarction

Abstract

fetched live from OpenAlex

Background MRI and fluorine 18–labeled sodium fluoride (18F-NaF) PET can be used to identify features of plaque instability, rupture, and disease activity, but large studies have not been performed. Purpose To evaluate the association between 18F-NaF activity and culprit carotid plaque in acute neurovascular syndrome. Materials and Methods In this prospective observational cohort study (October 2017 to January 2020), participants underwent 18F-NaF PET/MRI. An experienced clinician determined the culprit carotid artery based on symptoms and record review. 18F-NaF uptake was quantified using standardized uptake values and tissue-to-background ratios. Statistical significance was assessed with the Welch, χ2, Wilcoxon, or Fisher test. Multivariable models were used to evaluate the relationship between the imaging markers and the culprit versus nonculprit vessel. Results A total of 110 participants were evaluated (mean age, 68 years ± 10 [SD]; 70 men and 40 women). Of the 110, 34 (32%) had prior cerebrovascular disease, and 26 (24%) presented with amaurosis fugax, 54 (49%) with transient ischemic attack, and 30 (27%) with stroke. Compared with nonculprit carotids, culprit carotids had greater stenoses (≥50% stenosis: 30% vs 15% [P = .02]; ≥70% stenosis: 25% vs 4.5% [P < .001]) and had increased prevalence of MRI-derived adverse plaque features, including intraplaque hemorrhage (42% vs 23%; P = .004), necrotic core (36% vs 18%; P = .004), thrombus (7.3% vs 0%; P = .01), ulceration (18% vs 3.6%; P = .001), and higher 18F-NaF uptake (maximum tissue-to-background ratio, 1.38 [IQR, 1.12–1.82] vs 1.26 [IQR, 0.99–1.66], respectively; P = .04). Higher 18F-NaF uptake was positively associated with necrosis, intraplaque hemorrhage, ulceration, and calcification and inversely associated with fibrosis (P = .04 to P < .001). In multivariable analysis, carotid stenosis at or over 70% (odds ratio, 5.72 [95% CI: 2.2, 18]) and MRI-derived adverse plaque characteristics (odds ratio, 2.16 [95% CI: 1.2, 3.9]) were both associated with the culprit versus nonculprit carotid vessel. Conclusion Fluorine 18–labeled sodium fluoride PET/MRI characteristics were associated with the culprit carotid vessel in study participants with acute neurovascular syndrome. Clinical trial registration no. NCT03215550 and NCT03215563 © RSNA, 2022 Online supplemental material is available for this article.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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".

Quick stats

Citations20
Published2022
Admission routes1
Has abstractyes

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