Assessment of Baseline Imaging Features as Predictors of Poor Disease Course in Pediatric Onset Multiple Sclerosis (S49.009)
Notice bibliographique
Résumé
Objective: To determine whether conventional brain imaging features at onset predict subsequent disease activity in children with multiple sclerosis (MS). Background: Early identification of patients at risk of a poor disease course would aid treatment selection. Design/Methods: Participants with a diagnosis of MS and seronegative for AQP4 and MOG antibodies, were selected from a prospective cohort of children with incident demyelinating syndromes. Baseline (within 30 days of presentation) MRI features, and 2001, 2010 and 2017 international diagnostic criteria were evaluated using a validated scoring instrument. Relapses, normalized brain volume, and new lesions were recorded on follow-up (imaging at 0, 3, 6, 12 months and annually thereafter). Cox hazard models assessed baseline features predicting time to second attack and gaussian or binomial mixed effects models were applied for predicting normalized brain volume or new lesions; sensitivity, specificity and predictive values were computed for identifying patients with high frequency of attacks(≥3 in first two years). Results: 32 potential predictors were scored in 56 patients (42 F; median (IQR) age at onset 14.25 (12.92–15.40)y; 39 with clinical relapses; median (IQR) EDSS at two years 1 (0–2); 483 scans; median (IQR) follow-up 6.6 (4.8)y). Black holes at baseline (93% positive) were the best predictor of time to second attack (hazard ratio [95%CI]: 6 [0.8–46]), and predicted new lesions, either enhancing (HR [95%CI]: 12 [1.4–104]) or T2 hyperintense (HR [95% CI]: 6 [2–20]). Two year change in brain volume z-score was −0.15±0.09 and was not predicted significantly by any MRI feature, including baseline brain volume. Baseline 2010 criterion of dissemination in time best predicted the high relapse group (sensitivity/specificity 1/0.4; NPV/PPV 1/0.3). Baseline lesion count and T2 lesion volume were poor predictors. Conclusions: Conventional baseline imaging features poorly predict prognosis in children, motivating efforts to identify novel imaging or biological biomarkers of aggressive MS. Disclosure: Dr. Brown has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Biogen IDEC, NeuroRX. Dr. Fadda has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Atara Biotherapeutics INC. Dr. Longoni has nothing to disclose. Dr. Castro has nothing to disclose. Dr. O’Mahony has nothing to disclose. Dr. Bar-Or has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Atara Biotherapeutics, Biogen, Celgene/Receptos, Genentech/Roche, GlaxoSmithKline, Medimmune, Merck/EMD Serono, Novartis, Sanofi-Genzyme. Dr. Bar-Or has received research support from Atara Biotherapeutics, Biogen, Celgene/Receptos, Genentech/Roche, GlaxoSmithKline, Medimmune, Merck/EMD Serono, Novartis, Sanofi-Genzyme. Dr. Marrie has nothing to disclose. Dr. Yeh has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Novartis, Teva, Juno Therapeutics, ACI. Dr. Yeh has received research support from OIRM, MSSC/MSSF, NMSS, SickKids Foundation, CBMH, Guthy Jackson Foundation, CHAK, CMSC, SCN. Dr. Arnold has nothing to disclose. Dr. Banwell has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Novartis.
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 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 | 0,000 | 0,000 |
| 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 ».