Harmonic Hepatic US with Microbubble Contrast Agent: Initial Experience Showing Improved Characterization of Hemangioma, Hepatocellular Carcinoma, and Metastasis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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