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Record W4220707990 · doi:10.1002/jmri.28154

Prospective Evaluation of Virtual <scp>MR</scp> Elastography With Diffusion‐Weighted Imaging in Subjects With <scp>Nonalcoholic</scp> Fatty Liver Disease

2022· article· en· W4220707990 on OpenAlexafffund
Elyisha A. Hanniman, Andreu F. Costa, Chris V. Bowen, Mohamed Abdolell, Ashley Stueck, Magnus McLeod, Kevork Peltekian, James Rioux, Sharon E. Clarke

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2022
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsDalhousie University
FundersNova Scotia Research Innovation Trust
KeywordsNonalcoholic fatty liver diseaseMagnetic resonance elastographyMedicineFatty liverCirrhosisElastographyEffective diffusion coefficientDiffusion MRILiver biopsyFibrosisProspective cohort studyChronic liver diseaseMagnetic resonance imagingBiopsyRadiologyNuclear medicineInternal medicineUltrasoundDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is increasingly common worldwide and can lead to the development of cirrhosis, liver failure and cancer. Virtual magnetic resonance elastography (VMRE), which is based on a shifted apparent diffusion coefficient (sADC), is a potential noninvasive method to assess liver fibrosis without the specialized hardware and expertise required to implement traditional MR elastography (MRE). Although hepatic steatosis is known to confound ADC measurements, previous studies using VMRE have not corrected for hepatic fat fraction. PURPOSE: To compare VMRE, corrected for the confounding effects of unsuppressed fat signal, to MRE and biopsy in subjects with suspected NAFLD. STUDY TYPE: Prospective, cross-sectional. POPULATION: A total of 49 adult subjects with suspected NAFLD (18 male; median age 55 years, range 33-74 years) who underwent liver biopsy. FIELD STRENGTH/SEQUENCE: 3T, diffusion-weighted spin echo planar, chemical-shift encoded (IDEAL IQ) and MRE sequences. ASSESSMENT: Two observers drew regions of interest on sADC, proton density fat fraction and MRE-derived stiffness maps. Fat-corrected sADC values were used to calculate the diffusion-based shear modulus according to the VMRE method. Predicted fibrosis stage for MRE and VMRE was determined using previously published cut-off values. STATISTICAL TESTS: ). Agreement between MRE and VMRE-predicted fibrosis stage was evaluated with a kappa coefficient and accuracy compared using McNemar's test. A one-way ANOVA determined if the fat-corrected sADC (VMRE) and MRE differed by fibrosis stage. A P value < 0.05 was considered statistically significant. RESULTS: = 0.046 and a slope that was not significantly different from zero (P = 0.14). There was no agreement between MRE and VMRE-predicted fibrosis stage (kappa = -0.01). The proportion of correctly predicted fibrosis stage was significantly higher for MRE compared to VMRE. MRE was significantly associated with fibrosis stage, but fat-corrected sADC was not (P = 0.24). DATA CONCLUSION: Fat-corrected VMRE was not associated with fibrosis stage in NAFLD. Further investigation is required if VMRE is to be considered in subjects with NAFLD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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.

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

Citations26
Published2022
Admission routes2
Has abstractyes

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