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Record W4401800646 · doi:10.1016/j.mri.2024.110223

Inter-visit and inter-reader reproducibility of multi-parametric diffusion-weighted MR imaging in longitudinally imaged patients with metabolic dysfunction-associated fatty liver disease and healthy volunteers.

2024· article· en· W4401800646 on OpenAlexafffund
Anton Volniansky, Thierry Lefebvre, Merve Kulbay, Boyan Fan, Emre Aslan, Kim‐Nhien Vu, Emmanuel Montagnon, Bich Nguyen, Giada Sebastiani, Jeanne‐Marie Giard, Marie‐Pierre Sylvestre, Guillaume Gilbert, Guy Cloutier, An Tang

Bibliographic record

VenueMagnetic Resonance Imaging · 2024
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsPhilips (Canada)Université de MontréalMcGill University Health CentreCentre Hospitalier de l’Université de Montréal
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsReproducibilityMedicineFatty liverDiffusion MRIParametric statisticsDiseaseNuclear medicineMagnetic resonance imagingInternal medicineRadiologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Despite the widespread use of diffusion-weighted imaging (DWI) in metabolic dysfunction-associated fatty liver disease (MAFLD), MRI acquisition and quantification techniques vary in the literature suggesting the need for established and reproducible protocols. The goal of this study was to assess inter-visit and inter-reader reproducibility of DWI- and IVIM-derived parameters in patients with MAFLD and healthy volunteers using extensive sampling of the “fast” compartment, non-rigid registration, and exclusion voxels with poor fit quality. From June 2019 to April 2023, 31 subjects (20 patients with biopsy-proven MAFLD and 11 healthy volunteers) were included in this IRB-approved study. Subjects underwent MRI examinations twice within 40 days. 3.0 T DWI was acquired using a respiratory-triggered spin-echo diffusion-weighted echo-planar imaging sequence ( b -values of 0, 10, 20, 30, 40, 50, 100, 200, 400, 800 s/mm 2 ). DWI series were co-registered prior to voxel-wise non-linear regression of the IVIM model and voxels with poor fit quality were excluded (normalized root mean squared error ≥ 0.05). IVIM parameters (perfusion fraction, f ; diffusion coefficient, D ; and pseudo-diffusion coefficient, D* ), and apparent diffusion coefficients (ADC) were computed from manual segmentation of the right liver lobe performed by two analysts on two MRI examinations. All results are reported for f , D , D* , and ADC respectively. For inter-reader agreement on the first visit, ICC were of 0.985, 0.994, 0.986, and 0.993 respectively. For intra-reader agreement of analyst 1 assessed on both imaging examinations, ICC between visits were of 0.805, 0.759, 0.511, and 0.850 respectively. For inter-reader agreement on the first visit, mean bias and 95 % limits of agreement were (0.00 ± 0.03), (−0.01 ± 0.03) × 10 −3 mm 2 /s, (0.70 ± 10.40) × 10 −3 mm 2 /s, and (−0.02 ± 0.04) × 10 −3 mm 2 /s respectively. For intra-reader agreement of analyst 1, mean bias and 95 % limits of agreement were (0.01 ± 0.09) × 10 −3 mm 2 /s, (−0.01 ± 0.21) × 10 −3 mm 2 /s, (−13.37 ± 56.19) × 10 −3 mm 2 /s, and (−0.01 ± 0.16) × 10 −3 mm 2 /s respectively. Except for parameter D* that was associated with between-subjects parameter variability ( P = 0.009), there was no significant variability between subjects, examinations, or readers. With our approach, IVIM parameters f , D , D* , and ADC provided excellent inter-reader agreement and good to very good inter-visit or intra-reader agreement, thus showing the reproducibility of IVIM-DWI of the liver in MAFLD patients and volunteers.

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)0.001
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 score0.179
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.264
Teacher spread0.251 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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