Multiple Brain Developmental Venous Anomalies as a Marker for Constitutional Mismatch Repair Deficiency Syndrome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Biallelic constitutional mutations in DNA mismatch repair genes cause a distinct syndrome, constitutional mismatch repair deficiency syndrome (CMMRD), characterized by cancers from multiple organs, most commonly brain tumors, during childhood. Surveillance protocols include total and brain MR imaging among other modalities to enable early detection of tumors. Brain surveillance scans revealed prominent brain developmental venous anomalies (DVAs) in some patients. DVAs are benign vascular anomalies, and their incidence in the general population is 2.6%-6.4%. Most developmental venous anomalies are asymptomatic and are found incidentally. Our purpose was to assess the prevalence of DVAs in CMMRD patients and describe their phenotype. MATERIALS AND METHODS: A retrospective descriptive analysis of brain MR imaging studies from 10 patients from 3 families with CMMRD was performed. Analysis included the number of developmental venous anomalies, location, draining vessels, and associated vascular anomalies (ie, cavernomas), with clinical correlation of symptoms and tumors. RESULTS: All 10 patients had ≥2 developmental venous anomalies, and 2 had, in addition, non-therapy-induced cavernomas. There was no clinically symptomatic intracranial bleeding from developmental venous anomalies. Six patients had malignant brain tumors. The location of brain tumors was not adjacent to the developmental venous anomalies. No new developmental venous anomalies developed during follow-up. CONCLUSIONS: The occurrence of multiple developmental venous anomalies in all our patients with CMMRD suggests that developmental venous anomalies may be a characteristic of this syndrome that has not been previously described. If confirmed, this quantifiable feature can be added to the current scoring system and could result in early implementation of genetic testing and surveillance protocols, which can be life-saving for these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".