<scp>Magnetic Resonance Imaging</scp> Correlates of Multiple Sclerosis Immunopathological Patterns
Bibliographic record
Abstract
OBJECTIVE: Histology reveals that early active multiple sclerosis lesions can be classified into 3 main interindividually heterogeneous but intraindividually stable immunopathological patterns of active demyelination (patterns I-III). In patterns I and II, a T-cell- and macrophage-associated demyelination is suggested, with pattern II only showing signs of a humoral immune response. Pattern III is characterized by inflammatory lesions with an oligodendrocyte degeneration. Patterns suggest pathogenic heterogeneity, and we postulated that they have distinct magnetic resonance imaging (MRI) correlates that may serve as biomarkers. METHODS: We evaluated in an international collaborative retrospective cohort study the MRI lesion characteristics of 789 conventional prebiopsy and follow-up MRIs in relation to their histopathologically classified immunopathological patterns (n = 161 subjects) and lesion edge features (n = 112). RESULTS: A strong association of a ringlike enhancement and a hypointense T2-weighted (T2w) rim with patterns I and II, but not pattern III, was observed. Only a fraction of pattern III patients showed a ringlike enhancement, and this was always atypical. Ringlike enhancement and T2w rims colocalized, and ringlike enhancement showed a strong association with macrophage rims as shown by histology. A strong concordance of MRI lesion characteristics, meaning that different lesions showed the same features, was found when comparing biopsied and nonbiopsied lesions at a given time point, indicating lesion homogeneity within individual patients. INTERPRETATION: We provide robust evidence that MRI characteristics reflect specific morphological features of multiple sclerosis immunopatterns and that ringlike enhancement and T2w hypointense rims might serve as a valuable noninvasive biomarker to differentiate pathological patterns of demyelination. ANN NEUROL 2021;90:440-454.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".