Accuracy of MR Imaging–estimated Proton Density Fat Fraction for Classification of Dichotomized Histologic Steatosis Grades in Nonalcoholic Fatty Liver Disease
Bibliographic record
Abstract
PURPOSE: To evaluate the diagnostic performance of previously proposed high-specificity magnetic resonance (MR) imaging-estimated proton density fat fraction (PDFF) thresholds for diagnosis of steatosis grade 1 or higher (PDFF threshold of 6.4%), grade 2 or higher (PDFF threshold of 17.4%), and grade 3 (PDFF threshold of 22.1%) by using histologic findings as a reference in an independent cohort of adults known to have or suspected of having nonalcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS: This prospective, cross-sectional, institutional review board-approved, HIPAA-compliant single-center study was conducted in an independent cohort of 89 adults known to have or suspected of having NAFLD who underwent contemporaneous liver biopsy. MR imaging PDFF was estimated at 3 T by using magnitude-based low-flip-angle multiecho gradient-recalled-echo imaging with T2* correction and multipeak modeling. Steatosis was graded histologically (grades 0, 1, 2, and 3, according to the Nonalcoholic Steatohepatitis Clinical Research Network scoring system). Sensitivity, specificity, and binomial confidence intervals were calculated for the proposed MR imaging PDFF thresholds. RESULTS: The proposed MR imaging PDFF threshold of 6.4% to diagnose grade 1 or higher steatosis had 86% sensitivity (71 of 83 patients; 95% confidence interval [CI]: 76, 92) and 83% specificity (five of six patients; 95% CI: 36, 100). The threshold of 17.4% to diagnose grade 2 or higher steatosis had 64% sensitivity (28 of 44 patients; 95% CI: 48, 78) and 96% specificity (43 of 45 patients; 95% CI: 85, 100). The threshold of 22.1% to diagnose grade 3 steatosis had 71% sensitivity (10 of 14 patients; 95% CI: 42, 92) and 92% specificity (69 of 75 patients; 95% CI: 83, 97). CONCLUSION: In an independent cohort of adults known to have or suspected of having NAFLD, the previously proposed MR imaging PDFF thresholds provided moderate to high sensitivity and high specificity for diagnosis of grade 1 or higher, grade 2 or higher, and grade 3 steatosis. Prospective multicenter studies are now needed to further validate these high-specificity thresholds.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".