ABSTRACT 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.
Koch et al. (Wed,) studied this question.