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Record W2726826914 · doi:10.1007/s00330-017-4933-6

Repeatability of apparent diffusion coefficient and intravoxel incoherent motion parameters at 3.0 Tesla in orbital lesions

2017· article· en· W2726826914 on OpenAlexfundno aff
Augustin Lecler, Julien Savatovsky, Daniel Balvay, Mathieu Zmuda, Jean‐Claude Sadik, O. Galatoire, Frédérique Charbonneau, Olivier Bergès, Hervé Picard, Laure Fournier

Bibliographic record

VenueEuropean Radiology · 2017
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsnot available
FundersMcMaster University
KeywordsRepeatabilityIntravoxel incoherent motionNuclear medicineCoefficient of variationMedicineEffective diffusion coefficientMathematicsMagnetic resonance imagingRadiologyStatistics

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate repeatability of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in the orbit. METHODS: ) at 3.0T. Two readers independently delineated regions of interest in an orbital mass and in different intra-orbital and extra-orbital structures. Short-term test-retest repeatability and inter-observer agreement were assessed using the intra-class correlation coefficient (ICC), the coefficient of variation (CV) and Bland-Altman limits of agreements (BA-LA). RESULTS: Test-retest repeatability of IVIM parameters in the orbital mass was satisfactory for ADC and D (mean CV 12% and 14%, ICC 95% and 93%), poor for f and D*(means CV 43% and 110%, ICC 90% and 65%). Inter-observer repeatability agreement was almost perfect in the orbital mass for all the IVIM parameters (ICC = 95%, 93%, 94% and 90% for ADC, D, f and D*, respectively). CONCLUSIONS: IVIM appeared to be a robust tool to measure D in orbital lesions with good repeatability, but this approach showed a poor repeatability of f and D*. KEY POINTS: • IVIM technique is feasible in the orbit. • IVIM has a good-acceptable repeatability of D (CV range 12-25 %). • IVIM interobserver repeatability agreement is excellent (ICC range 90-95 %). • f or D* provide higher test-retest and interobserver variabilities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.294
Teacher spread0.258 · 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 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

Citations37
Published2017
Admission routes1
Has abstractyes

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