Diagnostic Utility of Dorsolateral Nigral Hyperintensity with Susceptibility Map‐Weighted Imaging in Degenerative Parkinsonism Compared to Other Movement Disorders
Bibliographic record
Abstract
BACKGROUND: Loss of dorsolateral nigral hyperintensity (DNH), also known as the swallow tail sign, on iron-sensitive MRI is a promising imaging marker for neurodegenerative parkinsonism. Susceptibility Map-Weighted Imaging (SMWI), an advanced MRI technique incorporating quantitative susceptibility mapping, has demonstrated superior contrast-to-noise ratio for detecting this feature, but has only been evaluated in small cohorts. OBJECTIVE: Determine the diagnostic accuracy of SMWI in degenerative parkinsonism and other neurological conditions. METHODS: We conducted a retrospective, diagnostic accuracy study of patients who underwent 3.0 T MRI with SMWI at our institution. Clinical diagnosis by neurologists served as the reference standard. Controls included patients with other movement disorders or neurological conditions. Two blinded readers assessed the presence of DNH independently, with discrepancies resolved by a senior neuroradiologist. RESULTS: A total of 248 patients were analyzed for the diagnostic accuracy of DNH using SMWI. Overall, the sensitivity was 92.0% (95% CI: 87.1-95.2%) and the specificity 95.8% (95% CI: 88.5-98.6%), for differentiating degenerative parkinsonism from other neurological conditions with excellent interrater reliability (κ = 0.88). The sensitivity for Parkinson's disease (PD) was 93.2% and 88.4% for atypical parkinsonism and remained high in early PD (88.2%) and in patients with mild motor disability (94.2%). CONCLUSIONS: SMWI is an accurate and reproducible imaging technique for diagnosing neurodegenerative parkinsonism, comparable to previously reported DAT-scan accuracy. Other sequences used to assess the DNH such as regular Susceptibility Weighted Imaging have shown variable results, particularly when studying disease controls. These findings support the adoption of SMWI as a standard commercial sequence in clinical MRI platforms.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".