MétaCan
Menu
Retour à la cohorte
Enregistrement W3112366708 · doi:10.1016/j.nicl.2020.102523

Multimodal MRI staging for tracking progression and clinical-imaging correlation in sporadic Creutzfeldt-Jakob disease

2020· article· en· W3112366708 sur OpenAlexaboutno aff
Simone Sacco, Matteo Paoletti, Adam M. Staffaroni, Huicong Kang, Julio C. Rojas, Gabe Marx, Sheng‐Yang M. Goh, Maria Luisa Mandelli, Isabel Elaine Allen, Joel H. Kramer, Stefano Bastianello, Roland G. Henry, Howie Rosen, Eduardo Caverzasi, Michael D. Geschwind

Notice bibliographique

RevueNeuroImage Clinical · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiquePrion Diseases and Protein Misfolding
Établissements canadiensnon disponible
Organismes subventionnairesNational Center for Research ResourcesNational Institute on AgingNational Institutes of HealthUniversità degli Studi di Pavia
Mots-clésThermal diffusivityDiffusion MRIMedicineMagnetic resonance imagingDiseaseAtrophyNuclear medicineImaging biomarkerBrain sizeRadiologyPathologyPhysics

Résumé

récupéré en direct d'OpenAlex

Diffusion imaging is very useful for the diagnosis of sporadic Creutzfeldt-Jakob disease, but it has limitations in tracking disease progression as mean diffusivity changes non-linearly across the disease course. We previously showed that mean diffusivity changes across the disease course follow a quasi J-shaped curve, characterized by decreased values in earlier phases and increasing values later in the disease course. Understanding how MRI metrics change over-time, as well as their correlations with clinical deficits are crucial steps in developing radiological biomarkers for trials. Specifically, as mean diffusivity does not change linearly and atrophy mainly occurs in later stages, neither alone is likely to be a sufficient biomarker throughout the disease course. We therefore developed a model combining mean diffusivity and Volume loss (MRI Disease-Staging) to take into account mean diffusivity's non-linearity. We then assessed the associations between clinical outcomes and mean diffusivity alone, Volume alone and finally MRI Disease-Staging. In 37 sporadic Creutzfeldt-Jakob disease subjects and 30 age- and sex-matched healthy controls, high angular resolution diffusion and high-resolution T1 imaging was performed cross-sectionally to compute z-scores for mean diffusivity (MD) and Volume. Average MD and Volume were extracted from 41 GM volume of interest (VOI) per hemisphere, within the images registered to the Montreal Neurological Institute (MNI) space. Each subject's volume of interest was classified as either "involved" or "not involved" using a statistical threshold of ± 2 standard deviation (SD) for mean diffusivity changes and/or -2 SD for Volume. Volumes of interest were MRI Disease-Staged as: 0 = no abnormalities; 1 = decreased mean diffusivity only; 2 = decreased mean diffusivity and Volume; 3 = normal ("pseudo-normalized") mean diffusivity, reduced Volume; 4 = increased mean diffusivity, reduced Volume. We correlated Volume, MD and MRI Disease-Staging with several clinical outcomes (scales, score and symptoms) using 4 major regions of interest (Total, Cortical, Subcortical and Cerebellar gray matter) or smaller regions pre-specified based on known neuroanatomical correlates. Volume and MD z-scores correlated inversely with each other in all four major ROIs (cortical, subcortical, cerebellar and total) highlighting that ROIs with lower Volumes had higher MD and vice-versa. Regarding correlations with symptoms and scores, higher MD correlated with worse Mini-Mental State Examination and Barthel scores in cortical and cerebellar gray matter, but subjects with cortical sensory deficits showed lower MD in the primary sensory cortex. Volume loss correlated with lower Mini-Mental State Examination, Barthel scores and pyramidal signs. Interestingly, for both Volume and MD, changes within the cerebellar ROI showed strong correlations with both MMSE and Barthel. Supporting using a combination of MD and Volume to track sCJD progression, MRI Disease-Staging showed correlations with more clinical outcomes than Volume or MD alone, specifically with Mini-Mental State Examination, Barthel score, pyramidal signs, higher cortical sensory deficits, as well as executive and visual-spatial deficits. Additionally, when subjects in the cohort were subdivided into tertiles based on their Barthel scores and their percentile of disease duration/course ("Time-Ratio"), subjects in the lowest (most impaired) Barthel tertile showed a much greater proportion of more advanced MRI Disease-Stages than the those in the highest tertile. Similarly, subjects in the last Time-Ratio tertile (last tertile of disease) showed a much greater proportion of more advanced MRI Disease-Stages than the earliest tertile. Therefore, in later disease stages, as measured by time or Barthel, there is overall more Volume loss and increasing MD. A combined multiparametric quantitative MRI Disease-Staging is a useful tool to track sporadic Creutzfeldt-Jakob- disease progression radiologically.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,238
Score d'incertitude au seuil0,749

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,052
Tête enseignante GPT0,396
Écart entre enseignants0,344 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations22
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueNeuroImage ClinicalMême sujetPrion Diseases and Protein MisfoldingTravaux en français237 207