A Novel Technology for Resolution of <scp>CEUS</scp> Imaging Problems in Patients With High <scp>BMI</scp> and Fatty Liver
Bibliographic record
Abstract
OBJECTIVES: In high-BMI patients with and without fatty liver, we evaluate performance of a commercially available specially designed ultrasound probe (SDP) for scanning at depth. Greyscale and contrast-enhanced ultrasound (CEUS) capability of SDP for parenchymal assessment and liver mass characterization, emphasizing HCC, is compared with standard curvilinear probes. METHODS: This retrospective study included 60 patients. Fifty-five with measured BMI included 46/55 (84%) overweight or obese, and 9/55(16%) in the normal range with severe fatty liver. Fifty-six patients with focal liver abnormality included 37 with a mass and 19 with post-ablative treatment site. Masses included 23 confirmed malignancies, 15 HCC, 4 ICC, and 4 metastases. SDP followed suboptimal ultrasound using a standard probe. Images with varying fat content were compared for depth of penetration on greyscale and ability of CEUS to diagnose tumors. RESULTS: SDP showed statistically significant improvement P = <.05 in CEUS penetration for all degrees of fatty liver (mild, moderate, and severe). In malignant tumors, SDP improved detection of lesion washout in the portal venous/late phase (PVP/LP) at depth >10 cm, and in all malignant masses (P < .05). Fifteen confirmed deep HCC showed arterial phase hyperenhancement on standard probe in 10/15 (67%) and 15/15 (100%) on SDP. PVP/LP washout on standard probe was shown in 4/15 (26%) and on SDP, 14/15, (93%). Therefore, 93% of LR-5 tumors were diagnosed with SDP. Removing necessity for biopsy. CONCLUSIONS: Metabolic syndrome and obesity challenge ultrasound, especially CEUS. SDP overcame limitations of standard probes for CEUS penetration especially in fatty liver. SDP was optimal for the liver mass characterization by detecting washout.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".