Comparative Evaluation of Tissue Attenuation Imaging (TAI) and Ultrasound Attenuation Parameter (UAP) for Noninvasive Quantification and Grading of Hepatic Steatosis
Bibliographic record
Abstract
BACKGROUND AND AIM: Hepatic steatosis is a major component of chronic liver disease and a key predictor of disease progression. The ultrasound attenuation parameter (UAP) is widely used via transient elastography (TE) for quantifying hepatic fat, but limited access and cost restrict its utility in routine practice. This study aimed to evaluate the correlation between tissue attenuation imaging (TAI) and UAP and to propose reference ranges for grading hepatic steatosis (S0-S3) using TAI as a noninvasive alternative. METHODS: This prospective observational study was conducted at Mann Scanning Centre, Jalandhar, Punjab, India. A total of 120 adult patients undergoing liver evaluation were included. All subjects underwent TE with UAP measurement and ultrasound-based TAI. Steatosis grading (S0-S3) was assigned based on UAP thresholds. Correlation between TAI and UAP was assessed using Spearman's and Pearson's coefficients. Receiver operating characteristic (ROC) curve analysis was performed to derive optimal TAI cutoffs corresponding to each steatosis grade. RESULTS: TAI showed a strong positive correlation with UAP (Spearman's ρ = 0.61, p < 0.001). TAI values increased progressively across steatosis grades S0 to S3. ROC analysis demonstrated an area under the curve (AUC) of 0.84 for detecting moderate-to-severe steatosis (≥ S2) using TAI. Proposed TAI thresholds for steatosis grading were: S0 (< 0.70 dB/cm/MHz), S1 (0.70-0.79 dB/cm/MHz), S2 (0.80-0.89 dB/cm/MHz), and S3 (≥ 0.90 dB/cm/MHz). The agreement between TAI-based and UAP-based grading was substantial (κ = 0.78). CONCLUSIONS: Tissue attenuation imaging is a reliable and accessible ultrasound-based technique for quantifying hepatic steatosis. It correlates well with UAP and can serve as a practical alternative for steatosis grading in settings where TE is unavailable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".