ArticleClinical transplantation2025
Deep Learning-Based MRI Volumetry for Living Kidney Donor Assessment: A New Tool for Predicting Post-Donation Renal Function.
Article in Clinical transplantation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A z-score-based reference system for renal volume using three-dimensional ultrasound: from model to clinical tool.Quantitative imaging in medicine and surgery · 2026Article
- Deep Learning-Based MRI Volumetry for Living Kidney Donor Assessment: A New Tool for Predicting Post-Donation Renal Function.Clinical transplantation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLiving kidney donation is a crucial option for addressing the global organ shortage and providing kidney transplantation for patients suffering from end-stage kidney disease. Ensuring donor safety necessitates a comprehensive preoperative assessment of kidney anatomy and function. This study evaluates the relationship between kidney volumes derived from deep learning-based MRI volumetry, intraoperative kidney volume measurements, split renal function measured by renal scintigraphy, and post-donation eGFR. Deep learning-based MRI volumetry is hypothesized to be a reliable method with good correlation.
methodsThis retrospective study analyzed 178 living kidney donors. Deep learning MRI volumetry-based kidney volumes were compared with intraoperative volumes of the explanted donor kidneys obtained using the water displacement method. Additionally, MRI-based volume ratios were compared with scintigraphy-based split renal function ratios to determine their ability to predict the kidney with poorer renal function and post-donation eGFR.
resultsDeep learning-based MRI volumetry strongly correlated with intraoperatively measured kidney volumes (Pearson's correlation; r = 0.7671; p < 0.0001), confirming its precision in volume estimation. Although MRI-based kidney volume ratios demonstrated only a moderate correlation with scintigraphy-based split renal function ratios (r = 0.4798), MRI volumetry correlated with 1-year post-donation eGFR. It tended to be better than renal scintigraphy (r = 0.6829 versus r = 0.6191).
conclusionDeep learning-based MRI volumetry is a reliable, non-invasive tool for estimating kidney volumes in living donors, offering a radiation-free alternative for preoperative assessment. While it differs from renal scintigraphy in evaluating split renal function ratios, its correlation with post-donation eGFR tends to be better, supporting its potential role in living kidney donor assessment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.