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Record W2585905508 · doi:10.1097/rli.0000000000000316

Quantitative Assessment of Liver Function Using Gadoxetate-Enhanced Magnetic Resonance Imaging

2016· article· en· W2585905508 on OpenAlexfundno aff
Leonidas Georgiou, Jeffrey Penny, Glynis Nicholls, Neil Woodhouse, François-Xavier Blé, Penny L. Hubbard Cristinacce, Josephine H. Naish

Bibliographic record

VenueInvestigative Radiology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicLanthanide and Transition Metal Complexes
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilMinistère des relations internationales et de la Francophonie
KeywordsMagnetic resonance imagingMedicineIn vivoReproducibilityBile ductNuclear medicineRadiologyChemistryInternal medicineBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to use noninvasive dynamic contrast-enhanced magnetic resonance imaging (MRI) techniques to study, in vivo, the distribution and elimination of the hepatobiliary contrast agent gadoxetate in the human body and characterize the transport mechanisms involved in its uptake into hepatocytes and subsequent efflux into the bile using a novel tracer kinetic model in a group of healthy volunteers. MATERIALS AND METHODS: Ten healthy volunteers (age range, 18-29 years), with no history of renal or hepatic impairment, were recruited via advertisement. Participants attended 2 MRI visits (at least a week apart) with gadoxetate as the contrast agent. Dynamic contrast-enhanced MRI data were acquired for approximately 50 minutes with a 3-dimensional gradient-echo sequence in the axial plane, at a temporal resolution of 6.2 seconds. Data from regions of interest drawn in the liver were analyzed using the proposed 2-compartment uptake and efflux model to provide estimates for the uptake rate of gadoxetate in hepatocytes and its efflux rate into the bile. Reproducibility statistics for the 2 visits were obtained to examine the robustness of the technique and its dependence in acquisition time. RESULTS: Eight participants attended the study twice and were included into the analysis. The resulting images provided the ability to simultaneously monitor the distribution of gadoxetate in multiple organs including the liver, spleen, and kidneys as well as its elimination through the common bile duct, accumulation in the gallbladder, and excretion in the duodenum. The mean uptake (ki) and efflux (kef) rates in hepatocytes, for the 2 visits using the 50-minute acquisition, were 0.22 ± 0.05 and 0.017 ± 0.006/min, respectively. The hepatic extraction fraction was estimated to be 0.19 ± 0.04/min. The variability between the 2 visits within the group level (95% confidence interval; ki: ±0.02/min, kef: ±0.004/min) was lower compared with the individual variability (repeatability; ki: ±0.06/min, kef: ±0.012/min). Data truncation demonstrated that the uptake rate estimates retained their precision as well as their group and individual reproducibility down to approximately 10 minutes of acquisition. Efflux rate estimates were underestimated (compared with the 50-minute acquisition) as the duration of the acquisition decreased, although these effects were more pronounced for acquisition times shorter than approximately 30 minutes. CONCLUSIONS: This is the first study that reports estimates for the hepatic uptake and efflux transport process of gadoxetate in healthy volunteers in vivo. The results highlight that dynamic contrast-enhanced MRI with gadoxetate can provide novel quantitative insights into liver function and may therefore prove useful in studies that aim to monitor liver pathology, as well as being an alternative approach for studying hepatic drug-drug interactions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.682

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 studies0.0000.001
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 head0.051
GPT teacher head0.299
Teacher spread0.249 · 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 designBench or experimental
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

Citations28
Published2016
Admission routes1
Has abstractyes

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