Ultrasound shear wave viscoelastography to characterize liver nodules
Bibliographic record
Abstract
Abstract Purpose . To investigate the diagnostic performance of ultrasound (US)-based shear wave speed (SWS), shear wave attenuation (SWA), and combination of them as shear wave viscoelastography (SWVE) methods in patients undergoing US to characterize focal liver nodules. Materials and methods . In this prospective cross-sectional study, 70 patients with 72 nodules were enrolled. Investigational US and clinical magnetic resonance imaging (MRI) examinations were performed in all participants. The composite reference standard included MRI or histopathology to differentiate benign and malignant nodules. A linear discriminant analysis (LDA) was used to assess the combination of SWVE methods. Analyzes included Mann–Whitney U test, receiver operating characteristic analysis, and computation of sensitivity and specificity at the point that maximized the Youden index. Results . Mean SWS was significantly higher in malignant than benign nodules (2.49 ± 0.76 m s −1 vs. 1.72 ± 0.70, p < 0.001), whereas SWA was lower (0.56 ± 0.30 vs. 1.10 ± 0.43 Np/m/Hz, p < 0.001). To differentiate between malignant and benign nodules, SWS with a threshold of 2.43 m s −1 achieved a sensitivity of 0.54 (95% confidence interval (CI): 0.38-0.69) and a specificity of 0.88 (CI: 0.74-0.95). SWA with a threshold of 0.81 Np/m/Hz yielded a sensitivity of 0.81 (CI: 0.66-0.90) and a specificity of 0.74 (CI: 0.58-0.86). Combining these SWVE methods using a LDA resulted in a sensitivity of 0.81 (CI: 0.66-0.91) and a specificity of 0.86 (CI: 0.71-0.94). Conclusion . Malignant nodules had higher SWS and lower SWA than benign ones. The combination of SWS and SWA in a LDA classification algorithm increased the diagnostic performance.
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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 | 0.000 |
| 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".