Vascular Soft-Tissue Tumors in Infancy: Distinguishing Features on Doppler Sonography
Bibliographic record
Abstract
OBJECTIVE: We describe the sonographic appearance and vascularization of three types of vascular tumors, including hemangioendothelioma, tufted angioma, and infantile myofibromatosis, and we determine whether vessel density and peak systolic shift can distinguish these tumors from angiomas and differentiate between the subtypes of these three entities. SUBJECTS AND METHODS: Our study included 16 infants with vascular tumors, other than hemangiomas, who were to undergo biopsy. We used gray-scale sonography to identify calcifications, to evaluate the borders of the lesions to determine whether they were poorly defined or well defined, and to determine the echogenicity relative to the surrounding soft tissue. Doppler sonography served to determine the number of vessels per square centimeter and the peak arterial Doppler shift. Sonographic findings were compared with the final diagnoses established by biopsy. RESULTS: The final diagnoses included five hemangioendotheliomas, six tufted angiomas, and five infantile myofibromatoses. Hemangioendotheliomas and tufted angiomas were ill defined compared with infantile myofibromatoses that were well defined. Only one vascular tumor, a hemangioendothelioma, fulfilled the diagnostic criteria of hemangioma. Tufted angiomas and infantile myofibromatoses were the least vascularized, with the lowest vessel density (zero to two vessels per square centimeter) and a relatively low systolic Doppler shift (0.7-1.0 kHz). CONCLUSION: The vascular tumors-hemangioendotheliomas, tufted angiomas, and infantile myofibromatoses-were distinguishable from hemangiomas on Doppler sonography in all cases except one hemangioendothelioma. Unlike hemangiomas, these lesions should be investigated by biopsy or excision.
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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 | 0.000 |
| 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 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".