Quantitative Ultrasound Spectroscopy for Differentiation of Hepatocellular Carcinoma from At-Risk and Normal Liver Parenchyma
Bibliographic record
Abstract
PURPOSE: Quantitative ultrasound approaches can capture tissue morphologic properties to augment clinical diagnostics. This study aims to clinically assess whether quantitative ultrasound spectroscopy (QUS) parameters measured in hepatocellular carcinoma (HCC) tissues can be differentiated from those measured in at-risk or healthy liver parenchyma. EXPERIMENTAL DESIGN: This prospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the Institutional Review Board. Fifteen patients with HCC, 15 non-HCC patients with chronic liver disease, and 15 healthy volunteers were included (31.1% women; 68.9% men). Ultrasound radiofrequency data were acquired in each patient in both liver lobes at two focal depths (3/9 cm). Region of interests (ROIs) were drawn on HCC and liver parenchyma. The average normalized power spectrum for each ROI was extracted, and a linear regression was fit within the -6 dB bandwidth, from which the midband fit (MBF), spectral intercept (SI), and spectral slope (SS) were extracted. Differences in QUS parameters between the ROIs were tested by a mixed-effects regression. RESULTS: = 0.57) were not significantly different in non-HCC tissue among the groups. CONCLUSIONS: The QUS parameters are significantly different in HCC versus non-HCC liver parenchyma, independent of underlying cirrhosis. This could be leveraged for improved HCC detection with ultrasound in the future.
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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 | 0.002 | 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 | 0.000 |
| 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".