Enhancing Liver Nodule Visibility and Diagnostic Classification Using Ultrasound Local Attenuation Coefficient Slope Imaging
Bibliographic record
Abstract
OBJECTIVE: B-mode ultrasound (US) presents challenges in accurately detecting and distinguishing between benign and malignant liver nodules. This study utilized quantitative US local attenuation coefficient slope (LACS) imaging to address these limitations. MATERIALS AND METHODS: This is a prospective, cross-sectional study in adult patients with definable solid liver nodules at US conducted from March 2021 to December 2023. The composite reference standard included histopathology when available or magnetic resonance imaging. LACS images were obtained using a phantom-free method. Nodule visibility was assessed by computing the contrast-to-noise ratio (CNR). Classification accuracy for differentiating benign and malignant lesions was assessed with the area under the receiver operating characteristic curve (AUC), along with sensitivity and specificity. RESULTS: The study enrolled 97 patients (age: 62 y ± 13 [standard deviation]), with 57.0% malignant and 43.0% benign observations (size: 26.3 ± 18.9 mm). LACS images demonstrated higher CNR (12.3 dB) compared to B-mode (p < 0.0001). The AUC for differentiating nodules and liver parenchyma was 0.85 (95% confidence interval [CI]: 0.79-0.90), with higher values for malignant (0.93, CI: 0.88-0.97) than benign nodules (0.76, CI: 0.66-0.87). A LACS threshold of 0.94 dB/cm/MHz provided a sensitivity of 0.83 (CI: 0.74-0.89) and a specificity of 0.82 (CI: 0.73-0.88). LACS mean values were higher (p < 0.0001) in malignant (1.28 ± 0.27 dB/cm/MHz) than benign nodules (0.98 ± 0.19 dB/cm/MHz). CONCLUSION: LACS imaging improves nodule visibility and provides better differentiation between benign and malignant liver nodules, showing promise as a diagnostic tool.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".