Magnetic Resonance Elastography to Assess Fibrosis in Kidney Allografts
Bibliographic record
Abstract
Background and objectives Fibrosis is a major cause of kidney allograft injury. Currently, the only means of assessing allograft fibrosis is by biopsy, an invasive procedure that samples <1% of the kidney. We examined whether magnetic resonance elastography, an imaging-based measure of organ stiffness, could noninvasively estimate allograft fibrosis and predict progression of allograft dysfunction. Design, setting, participants, & measurements Kidney allograft recipients >1 year post-transplant undergoing an allograft biopsy first underwent free-breathing, flow-compensated magnetic resonance elastography on a 3.0-T magnetic resonance imaging scanner. Each patient had serial eGFR measurements after the elastography scan for a follow-up period of up to 1 year. The mean stiffness value of the kidney allograft was compared with both the histopathologic Banff fibrosis score and the rate of eGFR change during the follow-up period. Results Sixteen patients who underwent magnetic resonance elastography and biopsy were studied (mean age: 54±9 years old). Whole-kidney mean stiffness ranged between 3.5 and 7.3 kPa. Whole-kidney stiffness correlated with biopsy-derived Banff fibrosis score (Spearman rho =0.67; P <0.01). Stiffness was heterogeneously distributed within each kidney, providing a possible explanation for the lack of a stronger stiffness-fibrosis correlation. We also found negative correlations between whole-kidney stiffness and both baseline eGFR (Spearman rho =−0.65; P <0.01) and eGFR change over time (Spearman rho =−0.70; P <0.01). Irrespective of the baseline eGFR, increased kidney stiffness was associated with a greater eGFR decline (regression r 2 =0.48; P =0.03). Conclusions Given the limitations of allograft biopsy, our pilot study suggests the potential for magnetic resonance elastography as a novel noninvasive measure of whole-allograft fibrosis burden that may predict future changes in kidney function. Future studies exploring the utility and accuracy of magnetic resonance elastography are needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".