Gadolinium-Enhanced Susceptibility-Weighted Imaging in Multiple Sclerosis: Optimizing the Recognition of Active Plaques for Different MR Imaging Sequences
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Gadolinium SWI is MR imaging that has recently been reported to be effective in the evaluation of several neurologic disorders, including demyelinating diseases. Our aim was to analyze the accuracy of gadolinium SWI for detecting the imaging evidence of active inflammation on MS plaques when a BBB dysfunction is demonstrated by a focal gadolinium-enhanced lesion and to compare this technique with gadolinium-enhanced T1 spin-echo and T1 spin-echo with magnetization transfer contrast. MATERIALS AND METHODS: MR imaging studies of 103 patients (170 examinations) were performed using a 1.5T scanner. Two neuroradiologists scrutinized signal abnormalities of the demyelinating plaques on gadolinium SWI and compared them with gadolinium T1 before and after an additional magnetization transfer pulse. Interrater agreement was evaluated among gadolinium T1 magnetization transfer contrast, gadolinium SWI, and gadolinium T1 spin-echo using the κ coefficient. The T1 magnetization transfer contrast sequence was adopted as the criterion standard in this cohort. Thus, the sensitivity, specificity, positive predictive value, and negative predictive value were calculated for gadolinium T1 spin-echo and gadolinium SWI sequences. RESULTS: Differences in BBB dysfunction were evident among gadolinium SWI, gadolinium T1 spin-echo, and gadolinium T1 magnetization transfer contrast. Gadolinium T1 magnetization transfer contrast demonstrated the highest number of active demyelinating plaques. Gadolinium SWI was highly correlated with gadolinium T1 magnetization transfer contrast in depicting acute demyelinating plaques (κ coefficient = 0.860; sensitivity = 0.837), and these techniques provided better performance compared with gadolinium T1 spin-echo (κ coefficient = 0.78; sensitivity = 0.645). CONCLUSIONS: Gadolinium SWI was able to better detect BBB dysfunction in MS plaques and had a better performance than gadolinium T1 spin-echo. Increasing SWI sequence applications in clinical practice can improve our knowledge of MS, likely allowing the addition of BBB dysfunction analysis to the striking findings of the previously reported central vein sign.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".