Soft-Tissue Congenital Hemangiomas and Related Complications: A Retrospective Cohort of 14 Cases With Emphasis on Prenatal Diagnosis, Counseling, and Postnatal Management
Bibliographic record
Abstract
Objectives: We aimed to describe prenatal imaging findings of superficial congenital hemangiomas (CH), review associated complications, and describe outcomes. Methods: A single-center retrospective cohort study spanning 18 years (2006–2024) reviewed imaging and medical charts of fetuses with soft-tissue masses suggestive of CH on prenatal ultrasound and/or magnetic resonance imaging, confirmed postnatally through evolution or histopathology. Results: All 14 cases (7 [50%] head and neck, 5 [36%] extremities, and 2 [14%] trunk) showed hypoechoic hypervascular masses on Doppler ultrasound with internal vascular ectasias, 12 (86%) being well circumscribed, and 5 (36%) having intralesional echogenic foci. Seven (50%) had a large draining vein and 5 (36%) showed right atrial enlargement. Magnetic resonance imaging (n = 11) demonstrated a well-circumscribed lesion in 9 (82%) cases, hypointense in 4 (36%), and iso- to hyperintense in 7 (64%) on T2-weighted imaging. Seven (64%) cases had internal T2-hypointense areas; none showed cystic areas or muscle invasion. One pregnancy was terminated. Of the 13 live births, all lesions showed complete or partial involution within a year; 3 (25%) developed superficial ulceration and 1 (8%) transient symptomatic heart failure. Four patients underwent elective surgery. Conclusion: Fetuses with CH had favorable outcomes, highlighting the importance of accurate prenatal diagnosis and delivery in specialized centers to manage potential complications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".