7-T MRI-based surrogate for histopathology examination of liver fibrosis
Bibliographic record
Abstract
Abstract Background To demonstrate that 7-T magnetic resonance imaging (MRI) provides a surrogate for histopathology of fresh ex vivo liver tissue, using the case study of liver fibrosis. Methods We prospectively enrolled 20 patients undergoing surgical liver resection between November 2021 and April 2023. Each ex vivo fresh liver tissue specimen (~ 1 cm 3 ) was sectioned in half. The first half, stained using Masson’s Trichrome and Perls, was assessed by three pathologists using the METAVIR score (reference standard). The second half was imaged with 7-T MRI using a cryoprobe (fat-suppressed T2-weighted turbo/fast spin-echo sequence, spatial resolution 75 × 75 × 200 µm 3 ) and assessed by three radiologists and the same three pathologists, using a newly developed MRI-METAVIR score. Results Five patients were excluded from the final analysis (one patient due to poor specimen quality, two due to surgery cancellation, and two previously published used for reader training). Of the remaining 15 patients, 10 (67%) presented with chronic liver diseases and 8/15 (53%) with advanced (F3 or F4) fibrosis. Radiologists achieved 88% sensitivity, 100% specificity, 93% accuracy (95% confidence interval 68–100%) and 0.94 Harrell’s c-index (0.86–1.00). Pathologists achieved 88% sensitivity, 86% specificity, 87% accuracy (60–98%) and 0.87 Harrell’s c-index (0.74–0.99). There were no statistically significant differences between MRI-based and pathologic reference standard stage ( p ≥ 0.655). Conclusion With an in-plane spatial resolution of ~ 75 × 75 µm 2 , MRI paralleled low-magnification histology, enabling the assessment of micro-architectural liver changes, and provided a surrogate for histopathology examination of fresh ex vivo liver tissue samples at a microscopic level. Relevance statement 7-T MRI provides a surrogate for histopathology visualisation of fresh ex vivo liver tissue, opening new research perspectives for clinical high-field MRI of the liver. Key Points Using the newly developed MRI-METAVIR score, 7-T MRI data strongly correlated with histopathology, achieving excellent agreement and accuracy. 7-T MRI accurately differentiated advanced from minimal liver fibrosis. 7-T MRI visualises liver micro-architecture, enabling pathology-like, noninvasive three-dimensional imaging. Graphical Abstract
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".