Comparing FIB-4, VCTE, pSWE, 2D-SWE, and MRE Thresholds and Diagnostic Accuracies for Detecting Hepatic Fibrosis in Patients with MASLD: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: To compare thresholds and accuracies of FIB-4, vibration-controlled transient elastography (VCTE), point shear wave elastography (pSWE), 2D shear wave elastography (2D-SWE), and MR elastography (MRE) for detecting hepatic fibrosis in patients with MASLD. Materials and Methods: Systematic searching of MEDLINE, EMBASE, Cochrane Library, Scopus, and the gray literature from inception to March 2024 was performed. Studies evaluating accuracies of FIB-4, VCTE, 2D-SWE, pSWE, and/or MRE for detecting significant (≥F2) and/or advanced (≥F3) hepatic fibrosis in MASLD patients compared to histology were identified. Full-text review and data extraction were performed independently by two reviewers. Multivariate meta-analysis and subgroup analyses were performed using index test and fibrosis grading. Risk of bias was assessed using QUADAS-2. Results: 207 studies with over 80,000 patient investigations were included. FIB-4 1.3 threshold sensitivity was 71% (95% CI 66–75%) for detecting advanced hepatic fibrosis, which improved to 88% (85–91%) using a <0.75 threshold. FIB-4 specificity using a 2.67 threshold was 96% (94–97%). Sensitivities of 88–91% were achieved using thresholds of 3.2 kPa for pSWE, 4.92 kPa for 2D-SWE, 7.18 kPa for VCTE, and 2.32 kPa for MRE. No significant differences were identified for sensitivities in subgroup analysis with thresholds between 7 and 9 kPa. Most imaging-based studies were high risk of bias for the index test. Conclusions: A FIB-4 threshold of <0.75 and modality-dependent thresholds (VCTE < 7 kPa; pSWE <3 kPa; 2D-SWE <5 kPa; and MRE <2.5 kPa) would achieve sensitivities of around 90% when defining low-risk MASLD in population screening. A modified two-tier algorithm aligning with existing Society of Radiologists in Ultrasound guidelines would improve risk stratification accuracies compared to existing guidelines by European and American liver societies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".