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Record W3006277828 · doi:10.1002/jmri.27064

Quantitative Susceptibility Mapping for Characterization of Intraplaque Hemorrhage and Calcification in Carotid Atherosclerotic Disease

2020· article· en· W3006277828 on OpenAlexaff
Chaoyue Wang, Yue Zhang, Jingwen Du, István N. Huszár, Saifeng Liu, Yongsheng Chen, Sagar Buch, Fang Wu, Yuehong Liu, Mark Jenkinson, Charlie Hsu, Zhaoyang Fan, E. Mark Haacke, Qi Yang

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsWestern University
FundersNatural Science Foundation of Beijing MunicipalityNational Natural Science Foundation of China
KeywordsMedicineQuantitative susceptibility mappingCalcificationKappaIntracerebral hemorrhageNuclear medicinePopulationRadiologyInternal medicineMagnetic resonance imagingMathematicsGeometry

Abstract

fetched live from OpenAlex

Background Carotid artery intraplaque hemorrhage (IPH), an unstable component of atherosclerosis, is associated with an increased risk of stroke. Purpose To investigate quantitative susceptibility mapping (QSM) as a tool for the evaluation of IPH and calcification in vivo. Study Type Prospective. Population Ten healthy volunteers and 15 patients. Field Strength/Sequence 3.0T Susceptibility‐weighted imaging (SWI), magnetization‐prepared rapid acquisition with gradient echo (MP‐RAGE), T1‐weighted sampling perfection with application of optimized contrasts using different flip angle evolution (T1‐SPACE), T2‐weighted turbo spin‐echo (T2WI), and time‐of‐flight (TOF) sequences. Assessment The vessel wall area of the carotid artery was measured with QSM and compared with T1‐SPACE on healthy volunteers. Four radiologists, blinded to clinical history and patient identity, determined the presence and area of IPH on MP‐RAGE and QSM, as well as the area of calcification on T1‐SPACE and QSM. Statistical Tests Bland–Altman analysis, Pearson correlation coefficients, linear regression analyses were performed to evaluate the concordance of area measurements. Cohen's kappa (κ) was analyzed to determine the agreement between IPH detections. The paired t‐test was used to compare the group differences. Results In 423 matched slices, 20.1% (85/423) and 19.6% (83/423) were detected to have IPH on MP‐RAGE and QSM, respectively. IPH detection by QSM and MP‐RAGE showed good agreement (κ = 0.822, P < 0.001) between the two methods. There was no significant difference in IPH area measurements between QSM and MP‐RAGE (7.28 mm2 ± 6.41 vs. 7.16 mm2 ± 5.99, P = 0.575). There was no significant difference in calcification area measurement between QSM and T1‐SPACE (3.51 mm2 ± 1.78 vs. 3.41 mm2 ± 2.02, P = 0.783). Data Conclusion QSM is a novel imaging tool for the identification of IPH in patients with carotid atherosclerosis and enables differentiation of IPH and calcification. Evidence Level 1 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2020;52:534–541.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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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Citations22
Published2020
Admission routes1
Has abstractyes

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