Imaging Review of Pediatric Monogenic CNS Vasculopathy with Genetic Correlation
Bibliographic record
Abstract
Monogenic cerebral vasculopathy is a rare but progressively recognizable cause of pediatric cerebral vasculopathy manifesting as early as fetal life. These monogenic cerebral vasculopathies can be silent or manifest variably as fetal or neonatal distress, neurologic deficit, developmental delay, cerebral palsy, seizures, or stroke. The radiologic findings can be nonspecific, but the presence of disease-specific cerebral and extracerebral imaging features can point to a diagnosis and guide genetic testing, allowing targeted treatment. The authors review the existing literature describing the frequently encountered and rare monogenic cerebral vascular disorders affecting young patients and describe the relevant pathogenesis, with an attempt to categorize them based on the defective step in vascular homeostasis and/or signaling pathways and characteristic cerebrovascular imaging findings. The authors also highlight the role of imaging and a dedicated imaging protocol in identification of distinct cerebral and extracerebral findings crucial in the diagnostic algorithm and selection of genetic testing. Early and precise recognition of these entities allows timely intervention, preventing or delaying complications and thereby improving quality of life. It is also imperative to identify the specific pathogenic variant and pattern of inheritance for satisfactory genetic counseling and care of at-risk family members. Last, the authors present an image-based approach to these young-onset monogenic cerebral vasculopathies that is guided by the size and predominant radiologic characteristics of the affected vessel with reasonable overlap. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".