Diagnosis and Prediction of Relapses in Susac Syndrome: A New Use for MR Postcontrast FLAIR Leptomeningeal Enhancement
Bibliographic record
Abstract
<h3>BACKGROUND AND PURPOSE:</h3> Leptomeningeal enhancement can be found in a variety of neurologic diseases such as Susac Syndrome. Our aim was to assess its prevalence and significance of leptomeningeal enhancement in Susac syndrome using 3T postcontrast fluid-attenuated inversion recovery MR imaging. <h3>MATERIALS AND METHODS:</h3> From January 2011 to December 2017, nine consecutive patients with Susac syndrome and a control group of 73 patients with multiple sclerosis or clinically isolated syndrome were included. Two neuroradiologists blinded to the clinical and ophthalmologic data independently reviewed MRIs and assessed leptomeningeal enhancement and parenchymal abnormalities. Follow-up MRIs (5.9 MRIs is the mean number per patient over a median period of 46 months) of patients with Susac syndrome were reviewed and compared with clinical and retinal fluorescein angiographic data evaluated by an independent ophthalmologist. Fisher tests were used to compare the 2 groups, and mixed-effects logistic models were used for analysis of clinical and imaging follow-up of patients with Susac syndrome. <h3>RESULTS:</h3> Patients with Susac syndrome were significantly more likely to present with leptomeningeal enhancement: 5/9 (56%) versus 6/73 (8%) in the control group (<i>P</i> = .002). They had a significantly higher leptomeningeal enhancement burden with ≥3 lesions in 5/9 patients versus 0/73 (<i>P</i> < .001). Regions of leptomeningeal enhancement were significantly more likely to be located in the posterior fossa: 5/9 versus 0/73 (<i>P</i> < .001). Interobserver agreement for leptomeningeal enhancement was good (κ = 0.79). There was a significant association between clinical relapses and increase of both leptomeningeal enhancement and parenchymal lesion load: OR = 6.15 (<i>P</i> = .01) and OR = 5 (<i>P</i> = .02), respectively. <h3>CONCLUSIONS:</h3> Leptomeningeal enhancement occurs frequently in Susac syndrome and could be helpful for diagnosis and in predicting clinical relapse.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".