Deep venous communication in vein of Galen malformations: incidence, Imaging, and Implications for treatment
Bibliographic record
Abstract
BACKGROUND: Failure to appreciate deep venous drainage pathways is a major cause of severe complications in the endovascular treatment of vein of Galen aneurysmal malformations (VOGMs). OBJECTIVE: To report deep venous drainage patterns in patients with VOGM, emphasizing the internal cerebral veins, and to describe the challenges in evaluating these. METHODS: Patients with VOGM presenting to our institute between 2000 and 2018 were retrospectively analyzed. Patients with complete and good quality imaging datasets were included in the study. Three neuroradiologists with expertise in the subject independently analyzed the deep venous drainage patterns on multi-sequence MRI and digital subtraction angiography. Follow-up imaging studies were analyzed for alterations in deep venous drainage patterns that occurred following endovascular treatment. Descriptive statistics were used to report findings. RESULTS: Twenty-three patients had optimal quality MRI imaging and 25 had optimal quality DSA imaging available. In 14/23 (61%) patients, internal cerebral vein (ICV) communication could be reliably identified on MRI and in 8/25 (32%) patients on DSA. Deep venous communication with the VOGM was demonstrated in 8/26 (30.8%) patients. One (3.8%) patient demonstrated ICV communication with the VOGM only on postoperative imaging, while in 2 (8%) patients the ICV drainage route changed from VOGM to alternative pathways after the procedure. Other variant pathways included lateral mesencephalic vein, superior or inferior sagittal sinus, anterior mesencephalic vein, tentorial sinus, deep Sylvian vein, and superior vermian vein. CONCLUSION: ICV communication with the VOGM is not uncommon and requires dedicated preprocedural imaging to identify it. However, there are significant challenges in assessing this communication in the presence of high-flow fistulae, vessel tortuosity and size, and contrast limitations in this population.
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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) | 0.000 | 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".