Sensitivity of multi-parametric quantitative magnetic resonance imaging for multiple sclerosis pathology
Bibliographic record
Abstract
BACKGROUND: In recent years, quantitative magnetic resonance imaging (MRI) made progress towards clinical applicability mainly through advances in acceleration techniques. In patients with multiple sclerosis (MS), objective quantitative MRI-based characterization of subtle pathological alterations in lesions, perilesion (PL), as well as normal-appearing (NA) white matter (NAWM) and grey matter (NAGM) would revolutionize clinical assessment. While numerous quantitative techniques have been applied in studies of MS patients, their diagnostic significance especially for individual patients with relatively short disease duration is unclear. Therefore, we investigated the sensitivity of several quantitative MRI parameters to focal and diffuse MS pathology in a clinical feasibility study with a small sample size. METHODS: In 13 MS patients with a mean disease duration of 8 years and a mean EDSS of 1.1 as well as 14 healthy age-matched controls (HC), we acquired nine (semi-)quantitative magnetic resonance (MR) biomarkers, namely myelin water fraction (MWF), magnetization transfer (MT) saturation (MTsat), inhomogeneous MT ratio (ihMTR), quantitative longitudinal relaxation time (qT1), intrinsic (qT2) and effective (qT2*) quantitative transverse relaxation times, proton density (PD), quantitative susceptibility mapping (QSM), and the ratio between T1-weighted and T2-weighted images (T1w/T2w). Four volumes of interest were automatically defined (NA/HC grey matter (GM), NA/HC white matter (WM), lesion, and PL), and biomarker values were analyzed between groups and tissue types. RESULTS: For all nine assessed biomarkers, mean values per patient were significantly different between lesion, PL, and NAWM (p < 0.05, FDR corrected). The lesion values of qT1, qT2, qT2 * , PD, and QSM were rather inhomogeneous. Furthermore, MWF, MTsat, and ihMTR were sensitive to diffuse WM pathology in MS with the largest absolute differences between NAWM and HCWM medians, albeit not statistically significant after correction for multiple testing. DISCUSSION: In our study, we successfully compared nine different quantitative MR parameters within the same subjects for tissue characterization of MS. Our study adds relevant aspects to the current debate on different sensitivities of various quantitative MR biomarkers to MS pathology. While all investigated MR biomarkers allowed characterizing lesions in individual patients, a separation of NAWM and HCWM could be most promising with the myelin-sensitive measures MWF, MTsat, and ihMTR.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".