Preoperative Diagnosis of Suprasellar Hemangioblastoma with Four-Dimensional Computed Tomography Angiography: Case Report and Literature Review
Bibliographic record
Abstract
Purpose Our case report presents the first case of suprasellar hemangioblastoma diagnosed preoperatively with dynamic computed tomography angiography (four-dimensional [4D] CTA) in a patient without Von Hippel-Lindau (VHL) disease. We illustrate the imaging characteristics of these exceedingly rare tumors and discuss the role of 4D CTA in confirming this diagnosis and guiding surgical management. Finally, we present a literature review of imaging findings, differential diagnosis, management, and prognosis. Case A 39-year-old woman known for diabetes mellitus type II and dyslipidemia presented with headache, bitemporal hemianopsia, and mild hyperprolactinemia. Initial diagnosis of suprasellar meningioma separate from pituitary gland was revised to definitive diagnosis of suprasellar hemangioblastoma after 4D CTA. Conclusion Suprasellar hemangioblastomas are extremely rare, often associated to VHL disease. They present as enhancing as suprasellar mass with prominent intra- and peritumoral vascular flow-voids on magnetic resonance imaging. 4D CTA confirms their vascular nature, demonstrates characteristic rapid shunting with feeding arteries, and enlarged draining veins, and is important in guiding surgical management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".