Vascular Soft-Tissue Tumors in Infancy: Distinguishing Features on Doppler Sonography
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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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it