The added value of quantitative ultrasound to shear wave elastography for assessment of steatohepatitis in a rat model
Bibliographic record
Abstract
Non-alcoholic fatty liver disease is a highly prevalent condition, which may progress to non-alcoholic steatohepatitis (NASH), an advanced form found in 3 to 5% of the population. As liver biopsy is invasive, there is a need for a non-invasive technique for the assessment of NASH. Due to promising results of shear wave elastography (SWE) in staging this disease, there is a high interest in developing a multi-parametric approach for assessment of liver steatosis within the same ultrasound (US) examination. The goal of this study was to assess the added value of quantitative US (QUS) parameters to SWE, based on random forest classifiers and areas under the ROC curve (AUC). Sixty male Sprague-Dawley rats were either fed a standard chow or a methionine- and choline-deficient diet. Using a research US system (model V1, Verasonics Inc.), SWE measurements were performed while rats were under anesthesia. To generate shear wavefronts within the liver, a linear array US transducer (ATL L7-4, Philips) was used to induce three 40-V 125-μs long radiation force pushes 4 mm apart. For SW tracking, the same transducer was used to acquire plane wave radiofrequency data at a frame rate of 4 kHz; images were reconstructed using the f-k migration algorithm. QUS acquisitions were performed using the same system and transducer. One hundred frames were acquired, migrated, and the echo envelope was obtained with Hilbert transforms. The image post-processing yielded 4 homodyned-K parametric maps within the region-of-interest (ROI), from which 8 features were extracted. The local attenuation coefficient slope within the ROI was also computed using the spectral shift method. QUS parameters improved the classification accuracy of steatohepatitis, liver steatosis, inflammation, and fibrosis compared to SWE alone. For detection of liver steatosis grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, ≤ 2 vs 3, respectively, AUCs increased from 0.70, 0.65, and 0.69 to 0.78, 0.78, and 0.75 (p <; 0.001).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".