Evolving Medical Imaging Techniques for the Assessment of Delayed Graft Function: A Narrative Review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Delayed graft function (DGF) is a significant complication that contributes to poorer graft function and shortened graft survival. In this review, we sought to evaluate the current and emerging role of medical imaging modalities in the assessment of DGF and how it may guide clinical management. SOURCES OF INFORMATION: PubMed, Google Scholar, and ClinicalTrial.gov up until February 2021. METHODS: This narrative review first examined the pathophysiology of DGF and current clinical management. We then summarized relevant studies that utilized medical imaging to assess posttransplant renal complications, namely, DGF. We focused our attention on noninvasive, evolving imaging modalities with the greatest potential for clinical translation, including contrast-enhanced ultrasound (CEUS) and multiparametric magnetic resonance imaging (MRI). KEY FINDINGS: A kidney biopsy in the setting of DGF can be used to assess the degree of ischemic renal injury and to rule out acute rejection. Biopsies are accompanied by complications and may be limited by sampling bias. Early studies on CEUS and MRI have shown their potential to distinguish between the 2 most common causes of DGF (acute tubular necrosis and acute rejection), but they have generally included only small numbers of patients and have not kept pace with more recent technical advances of these imaging modalities. There remains unharnessed potential with CEUS and MRI, and more robust clinical studies are needed to better evaluate their role in the current era. LIMITATIONS: The adaptation of emerging approaches for imaging DGF will depend on additional clinical trials to study the feasibility and diagnostic test characteristics of a given modality. This is limited by access to devices, technical competence, and the need for interdisciplinary collaborations to ensure that such studies are well designed to appropriately inform clinical decision-making.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".