Harmonic Hepatic US with Microbubble Contrast Agent: Initial Experience Showing Improved Characterization of Hemangioma, Hepatocellular Carcinoma, and Metastasis
Bibliographic record
Abstract
PURPOSE: To characterize blood flow in focal hepatic lesions with harmonic ultrasonographic (US) imaging and a microbubble contrast agent. MATERIALS AND METHODS: Thirty patients with known hepatic masses were examined after injection of a perfluorocarbon microbubble agent. Tumor vascularity was assessed with continuous, harmonic gray-scale imaging with a low mechanical index (MI). Tumor vascular volume was assessed with brief, high-MI insonation called interval-delay imaging, which caused microbubble destruction. As the total contrast agent volume in the liver reflects the total vascular volume, quantitation of lesion enhancement relative to normal hepatic enhancement helped determine the vascular volume of the tumor relative to that of normal parenchyma. RESULTS: Low-MI continuous harmonic imaging showed lesional vessels in hepatocellular carcinomas, minimal or no vessels in hemangiomas, and variable vascularization in metastases. High-MI interval-delay imaging showed greater enhancement in hepatocellular carcinomas than in normal liver (P <.02) and showed less enhancement in hemangiomas than in normal liver (P <.02). Enhancement in metastases was greater in the margins than in the center; as a result, the lesions appeared smaller (P <.03) and less well defined on the interval-delay images. CONCLUSION: Contrast-enhanced harmonic imaging appears superior to conventional Doppler US for hepatic mass characterization. Low-MI continuous and high-MI interval-delay imaging can help assess tumor vascular pattern and microvascular volume.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".