MAGNETIC RESONANCE IMAGING BY DIFFUSION TENSOR IMAGING- MICROSTRUCTURAL THALAMIC CHANGES IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
PV041 / #676 Poster Topic: AS05 - CNS Lupus Background/Purpose Neuroimaging plays a crucial role in identifying neurological abnormalities in patients with Systemic Lupus Erythematosus (SLE), particularly in those with central nervous system involvement. Diffusion Tensor Imaging (DTI) is an advanced magnetic resonance imaging (MRI) technique that maps the brain’s microstructure by measuring fractional anisotropy (FA) and mean diffusivity (MD), providing additional insights beyond conventional MRI. DTI is especially useful when conventional MRI does not reveal significant findings, and it aids in both early disease detection and monitoring progression. Methods The study involved 58 childhood-onset SLE (cSLE) patients, 68 adult-onset SLE (aSLE) patients, and 60 healthy controls (HC). All participants underwent clinical, neurological, and laboratory evaluations, including disease activity and damage assessments using the SLE Disease Activity Index (SLEDAI) and Systemic Lupus International Collaborating Clinics (SLICC) scales. Mood and anxiety disorders were evaluated using the Beck Inventory, while cognitive function was assessed with the Montreal Cognitive Assessment (MoCA) (Table 1). MRI scans were performed using a Philips 3 Tesla scanner, with thalamus segmentation on T1-weighted images using FreeSurfer. Diffusion-weighted images (DWI) were analyzed using the FSL tool, and DTI scalar maps of FA, MD, Axial Diffusivity (AD), and Radial Diffusivity (RD) were generated. Mean and standard deviation values for these parameters were calculated for each thalamic region. A p-value ≤0.05 was considered statistically significant. Table 1. Demographic data, laboratory findings, neuropsychiatric manifestations, and treatment in cSLE, aSLE, and the controls. Results No significant difference in thalamic volume was found between cSLE (mean volume 12641.4mm³, SD=1571.9) and aSLE patients (mean volume 12521.3mm³, SD=1582.9). However, both groups showed significantly reduced thalamic volumes compared to the HC group (mean volume 13990.8mm³, SD=1621.8, p<0.001). DTI analysis revealed significant differences in FA, MD, RD, and AD values between the SLE groups and HC. We observed significantly lower FA values between the cSLE and HC groups in the following regions: left intralaminar (p=0.034), right anterolateral (p=0.09), right lateral caudal (p=0.005), right intralaminar (p=0.02) and right posterior (p=0.04). Significantly higher MD values between the cSLE and HC groups in the left anterolateral (p=0.047) and right anterolateral (p<0.001) regions; between the cSLE and aSLE groups in the right anterolateral region (p=0.02); and between the aSLE and HC groups in the left medial (p=0.038) and left posterior (p=0.05) regions. Significantly higher RD values between the cSLE and HC groups in the left anterolateral (p=0.046), right anterolateral (p<0.001), right caudal lateral (p=0.016), and right medial (p=0.037) regions; between the cSLE and aSLE groups in the right anterolateral region (p=0.03); and between the aSLE and HC groups in the left medial (p=0.039) and left posterior (p=0.01) regions. Significantly higher AD values between the cSLE and HC groups in the left medial region (p=0.047); and between the aSLE and HC groups in the left posterior region (p=0.006). Conclusions SLE patients exhibit reduced thalamic volume compared to HC, with more pronounced microstructural changes observed in cSLE patients compared to aSLE. These findings suggest that cSLE is associated with greater thalamic involvement and microstructural alterations. Longitudinal studies are needed to determine whether these microstructural changes are transient or permanent.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».