Could MRI Be Used To Image Kidney Fibrosis? A Review of Recent Advances and Remaining Barriers
Bibliographic record
Abstract
A key contributor to the progression of nearly all forms of CKD is fibrosis, a largely irreversible process that drives further kidney injury. Despite its importance, clinicians currently have no means of noninvasively assessing renal scar, and thus have historically relied on percutaneous renal biopsy to assess fibrotic burden. Although helpful in the initial diagnostic assessment, renal biopsy remains an imperfect test for fibrosis measurement, limited not only by its invasiveness, but also, because of the small amounts of tissue analyzed, its susceptibility to sampling bias. These concerns have limited not only the prognostic utility of biopsy analysis and its ability to guide therapeutic decisions, but also the clinical translation of experimental antifibrotic agents. Recent advances in imaging technology have raised the exciting possibility of magnetic resonance imaging (MRI)-based renal scar analysis, by capitalizing on the differing physical features of fibrotic and nonfibrotic tissue. In this review, we describe two key fibrosis-induced pathologic changes (capillary loss and kidney stiffening) that can be imaged by MRI techniques, and the potential for these new MRI-based technologies to noninvasively image renal scar.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".