Prenatal diagnosis and conservative management of a congenital cavernous sinus infantile hemangioma: illustrative case
Bibliographic record
Abstract
BACKGROUND: Infantile hemangiomas (IHs) are the most common benign vascular tumors of infancy, although intracranial IHs remain exceedingly rare. Diagnosis is typically postnatal, with some cases requiring medical or surgical intervention. Here, the authors report the first case of a congenital cavernous sinus IH diagnosed via prenatal imaging and managed conservatively with successful spontaneous regression. OBSERVATIONS: A 28-year-old female underwent routine fetal ultrasound at 32 weeks' gestation, revealing a 1.8-cm perithalamic lesion. Fetal MRI at 33 weeks confirmed an extra-axial mass in the right middle fossa with a dural attachment. Postnatal MRI on day 1 of life demonstrated imaging features consistent with an IH. Given the neonate's asymptomatic status, conservative management was pursued. Serial MRI at 10 weeks and 6 months showed progressive lesion regression, with near-complete resolution. The infant remained asymptomatic with normal neurodevelopmental progress. LESSONS: This case highlights the potential for spontaneous regression of congenital intracranial IHs diagnosed prenatally. Despite the risk of bleeding, conservative management with close clinical and radiological follow-up may be a viable option in select asymptomatic cases, particularly in the neonatal period when surgical risks are significant. https://thejns.org/doi/10.3171/CASE25274.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".