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Record W2969964521 · doi:10.1002/jmri.26904

Reliability of 3D texture analysis: A multicenter MRI study of the brain

2019· article· en· W2969964521 on OpenAlexafffundabout
Daniel Ta, Muhammad Haris Khan, Abdullah Ishaque, Peter Seres, Dean T. Eurich, Yee‐Hong Yang, Sanjay Kalra

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2019
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsFondation Brain CanadaALS Society of Canada
KeywordsIntraclass correlationReliability (semiconductor)NeuroimagingVoxelNuclear medicineComputer sciencePsychologyReproducibilityMedicineArtificial intelligenceMathematicsStatisticsNeurosciencePhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Texture analysis (TA) is an image-analysis technique that detects complex intervoxel statistical patterns. 3D TA has shown potential in detecting cerebral degeneration not perceptible to the human eye in many neurological disorders. The reliability of this method's application in a multicenter study is unknown. PURPOSE: To assess the intrasite and intersite reliability of a novel 3D TA method from data acquired systematically from the Canadian ALS Neuroimaging Consortium (CALSNIC). STUDY TYPE: Prospective multicenter data with harmonized MR sequence parameters acquired from five sites. POPULATION: Six healthy subjects. FIELD STRENGTH: -weighted MRI of the brain. ASSESSMENT: Voxel-based 3D TA was performed on the whole brain to produce texture maps. STATISTICAL TESTS: Intra- and intersite reliability of texture features was assessed using a two-way mixed-effects model for intraclass correlation coefficients (ICC). ICCs were calculated in a region-of-interest (ROI) analysis of predetermined anatomically relevant areas. A voxelwise approach was used to assess the whole brain. RESULTS: In the ROI analyses, intrasite reliability was excellent (ICC > 0.75) across most regions and texture features (autocorrelation [autoc], contrast [contr], energy [energ]). Intersite reliability was excellent for most regions with autoc, ranging from fair to excellent for contr, and ICCs ranging from poor to good (<0.40-0.75) for energ. Voxelwise analyses revealed a large range in ICC across the brain for both intrasite and intersite ICCs (0.0-0.90), with higher reliability in the cortical gray matter compared with deeper subcortical structures. DATA CONCLUSION: Overall, the reliability of 3D TA was highly dependent on texture feature, region studied, and method of analysis (ROI or voxelwise). Intrasite reproducibility was good to excellent, and better than intersite. ROI-based analyses present higher reliability in comparison with voxelwise analyses. Autoc has overall excellent reliability. These factors might be considered when designing future 3D TA studies. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1200-1209.

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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)0.002
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 score0.046
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.272
Teacher spread0.268 · 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

Citations25
Published2019
Admission routes3
Has abstractyes

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