Three‐Dimensional Lesion Phenotyping and Physiologic Characterization Inform Remyelination Ability in Multiple Sclerosis
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND PURPOSE Multiple sclerosis (MS) clinical management is based upon lesion characterization from 2‐dimensional (2D) magnetic resonance imaging (MRI) views. Such views fail to convey the lesion‐phenotype (ie, shape and surface texture) complexity, underlying metabolic alterations, and remyelination potential. We utilized a 3‐dimensional (3D) lesion phenotyping approach coupled with imaging to study physiologic profiles within and around MS lesions and their impacts on lesion phenotypes. METHODS Lesions were identified in 3T T 2 ‐FLAIR images and segmented using geodesic active contouring. A calibrated fMRI sequence permitted measurement of cerebral blood flow (CBF), blood‐oxygen‐level‐dependent signal (BOLD), and cerebral metabolic rate of oxygen (CMRO 2 ). These metrics were measured within lesions and surrounding tissue in concentric layers exact to the 3D‐lesion shape. BOLD slope was calculated as BOLD changes from a lesion to its surrounding perimeters. White matter integrity was measured using diffusion kurtosis imaging. Associations between these metrics and 3D‐lesion phenotypes were studied. RESULTS One hundred nine lesions from 23 MS patients were analyzed. We identified a noninvasive biomarker, BOLD slope, to metabolically characterize lesions. Positive BOLD slope lesions were metabolically active with higher CMRO 2 and CBF compared to negative BOLD slope or inactive lesions. Metabolically active lesions with more intact white matter integrity had more symmetrical shapes and more complex surface textures compared to inactive lesions with less intact white matter integrity. CONCLUSION The association of lesion phenotypes with their metabolic signatures suggests the prospect for translation of such data to clinical management by providing information related to metabolic activity, lesion age, and risk for disease reactivation and self‐repair. Our findings also provide a platform for disease surveillance and outcome quantification involving myelin repair therapeutics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".