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Record W3083729105 · doi:10.1007/s00330-020-07182-0

Whole-body MRI versus an FDG-PET/CT-based reference standard for staging of paediatric Hodgkin lymphoma: a prospective multicentre study

2020· article· en· W3083729105 on OpenAlexaff
Suzanne Spijkers, Annemieke S. Littooij, Thomas C. Kwee, Nelleke Tolboom, Auke Beishuizen, Marrie C.A. Bruin, Sjoerd G. Elias, Tim van de Brug, Goya Enríquez, Constantino Sábado, Elka Miller, Claudio Granata, Charlotte de Lange, Federico Verzegnassi, Mary‐Louise C. Greer, Rutger A. J. Nievelstein

Bibliographic record

VenueEuropean Radiology · 2020
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick ChildrenUniversity of Ottawa
FundersStichting Kinderen Kankervrij
KeywordsMedicineConcordanceNeuroradiologyRadiologyNuclear medicineKappaStage (stratigraphy)Cohen's kappaProspective cohort studyMagnetic resonance imagingPositron emission tomographyStandardized uptake valuePET-CTNeurologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the concordance of whole-body MRI (WB-MRI) and an FDG-PET/CT-based reference standard for the initial staging in children with Hodgkin lymphoma (HL) METHODS: Children with newly diagnosed HL were included in this prospective, multicentre, international study and underwent WB-MRI and FDG-PET/CT at staging. Two radiologists and a nuclear medicine physician independently evaluated all images. Discrepancies between WB-MRI and FDG-PET/CT were assessed by an expert panel. All FDG-PET/CT errors were corrected to derive the FDG-PET/CT-based reference standard. The expert panel corrected all reader errors in the WB-MRI DWI dataset to form the intrinsic MRI data. Inter-observer agreement for WB-MRI DWI was calculated using overall agreement, specific agreements and kappa statistics. Concordance for correct classification of all disease sites and disease stage between WB-MRI (without DWI, with DWI and intrinsic WB-MRI DWI) and the reference standard was calculated as primary outcome. Secondary outcomes included positive predictive value, negative predictive value and kappa statistics. Clustering within patients was accounted for using a mixed-effect logistic regression model with random intercepts and a multilevel kappa analysis. RESULTS: Sixty-eight children were included. Inter-observer agreement between WB-MRI DWI readers was good for disease stage (κ = 0.74). WB-MRI DWI agreed with the FDG-PET/CT-based reference standard for determining disease stage in 96% of the patients versus 88% for WB-MRI without DWI. Agreement between WB-MRI DWI and the reference standard was excellent for both nodal (98%) and extra-nodal (100%) staging. CONCLUSIONS: WB-MRI DWI showed excellent agreement with the FDG-PET/CT-based reference standard. The addition of DWI to the WB-MRI protocol improved the staging agreement. KEY POINTS: • This study showed excellent agreement between WB-MRI DWI and an FDG-PET/CT-based reference standard for staging paediatric HL. • Diffusion-weighted imaging is a useful addition to WB-MRI in staging paediatric HL. • Inter-observer agreement for WB-MRI DWI was good for both nodal and extra-nodal staging and determining disease stage.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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