Dominik Thomas Koch, Felix Oliver Hofmann, Dimitrios Trompoukis, Malte Schirren, Severin Jacobi, Tobias Seibt, Stephan Kemmner, Maximilian Scheifele, Matthias Ilmer, Bernhard Renz, Jens Werner, Manfred Stangl, Markus Guba, Dionysios Koliogiannis
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Deep learning-based MRI volumetry is hypothesized to be a reliable method with good correlation.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>This 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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Deep learning-based MRI volumetry strongly correlated with intraoperatively measured kidney volumes (Pearson's correlation; <i>r</i> = 0.7671; <i>p</i> < 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 (<i>r</i> = 0.4798), MRI volumetry correlated with 1-year post-donation eGFR. It tended to be better than renal scintigraphy (<i>r</i> = 0.6829 versus <i>r</i> = 0.6191).</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Deep 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.</p>\n </section>\n </div>","PeriodicalId":10467,"journal":{"name":"Clinical Transplantation","volume":"39 10","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503087/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep Learning-Based MRI Volumetry for Living Kidney Donor Assessment: A New Tool for Predicting Post-Donation Renal Function\",\"authors\":\"Dominik Thomas Koch, Felix Oliver Hofmann, Dimitrios Trompoukis, Malte Schirren, Severin Jacobi, Tobias Seibt, Stephan Kemmner, Maximilian Scheifele, Matthias Ilmer, Bernhard Renz, Jens Werner, Manfred Stangl, Markus Guba, Dionysios Koliogiannis\",\"doi\":\"10.1111/ctr.70338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Living 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>This 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Deep learning-based MRI volumetry strongly correlated with intraoperatively measured kidney volumes (Pearson's correlation; <i>r</i> = 0.7671; <i>p</i> < 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 (<i>r</i> = 0.4798), MRI volumetry correlated with 1-year post-donation eGFR. 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Deep Learning-Based MRI Volumetry for Living Kidney Donor Assessment: A New Tool for Predicting Post-Donation Renal Function
Background
Living 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.
Methods
This 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.
Results
Deep 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).
Conclusion
Deep 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.
期刊介绍：
Clinical Transplantation: The Journal of Clinical and Translational Research aims to serve as a channel of rapid communication for all those involved in the care of patients who require, or have had, organ or tissue transplants, including: kidney, intestine, liver, pancreas, islets, heart, heart valves, lung, bone marrow, cornea, skin, bone, and cartilage, viable or stored.
Published monthly, Clinical Transplantation’s scope is focused on the complete spectrum of present transplant therapies, as well as also those that are experimental or may become possible in future. Topics include:
Immunology and immunosuppression;
Patient preparation;
Social, ethical, and psychological issues;
Complications, short- and long-term results;
Artificial organs;
Donation and preservation of organ and tissue;
Translational studies;
Advances in tissue typing;
Updates on transplant pathology;.
Clinical and translational studies are particularly welcome, as well as focused reviews. Full-length papers and short communications are invited. Clinical reviews are encouraged, as well as seminal papers in basic science which might lead to immediate clinical application. Prominence is regularly given to the results of cooperative surveys conducted by the organ and tissue transplant registries.
Clinical Transplantation: The Journal of Clinical and Translational Research is essential reading for clinicians and researchers in the diverse field of transplantation: surgeons; clinical immunologists; cryobiologists; hematologists; gastroenterologists; hepatologists; pulmonologists; nephrologists; cardiologists; and endocrinologists. It will also be of interest to sociologists, psychologists, research workers, and to all health professionals whose combined efforts will improve the prognosis of transplant recipients.
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