Morphology-Specific Discrimination between MS White Matter Lesions and Benign White Matter Hyperintensities Using Ultra-High-Field MRI
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recently published North American Imaging in Multiple Sclerosis guidelines call for derivation of a specific radiologic definition of MS WM lesions and mimics. The purpose of this study was to use SWI and magnetization-prepared FLAIR images for sensitive differentiation of MS from benign WM lesions using the morphologic characteristics of WM lesions. MATERIALS AND METHODS: Seventeen patients with relapsing-remitting MS and 18 healthy control subjects were enrolled retrospectively. For each subject, FLAIR and multiecho gradient-echo images were acquired using 7T MR imaging. Optimized postprocessing was used to generate single-slice SWI of cerebral veins. SWI/FLAIR images were registered, and 3 trained readers performed lesion assessment. Morphology, location of lesions, and the time required for assessment were recorded. Analyses were performed on 3 different pools: 1) lesions of >3 mm, 2) nonconfluent lesions of >3 mm, and 3) nonconfluent lesions of >3 mm with no or a single central vein. RESULTS: The SWI/FLAIR acquisition and processing protocol enabled effective assessment of central veins and hypointense rims in WM lesions. Assessment of nonconfluent lesions with ≥1 central vein enabled the most specific and sensitive differentiation of patients with MS from controls. A threshold of 67% perivenous WM lesions separated patients with MS from controls with a sensitivity of 94% and specificity of 100%. Lesion assessment took an average of 12 minutes 10 seconds and 4 minutes 33 seconds for patients with MS and control subjects, respectively. CONCLUSIONS: Nonconfluent lesions of >3 mm with ≥1 central vein were the most sensitive and specific differentiators between patients with MS and control subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".