Assessment of Baseline Imaging Features as Predictors of Poor Disease Course in Pediatric Onset Multiple Sclerosis (S49.009)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".