CT-based Automated Volumetry as a Biomarker of Global and Split Renal Function in Living Kidney Donors
Fink, A.; Burzer, F.; Sacalean, V.; Rau, S.; Kaestingschaefer, K. F.; Rau, A.; Koettgen, A.; Bamberg, F.; Jaenigen, B.; Russe, M. F.
Show abstract
BackgroundKidney volumetry derived from CT has been proposed as a surrogate of renal function in living kidney donor evaluation. However, clinical integration has been limited by reader-dependent workflows and semiautomatic methods susceptible to image quality. PurposeTo evaluate whether fully automated CT-based segmentation of renal cortex, medulla and total parenchymal volume provides reproducible volumetric biomarkers associated with global and split renal function in living kidney donor candidates. Materials and MethodsIn this retrospective single-center study, 461 living kidney donor candidates (2003-2021) underwent contrast-enhanced abdominal CT. A convolutional neural network was trained to automatically segment cortical, medullary, and total parenchymal volumes on arterial-phase images. Segmentation performance was evaluated against manual reference annotations. Volumes were indexed to body surface area. Associations with eGFR, 24-hour creatinine clearance, cystatin C, and tubular clearance were assessed using Spearman correlation coefficient ({rho}), and side-specific volume fractions were compared with scintigraphy -derived split function. ResultsAutomated segmentation achieved excellent agreement with expert reference segmentations (Dice 0.95 for cortex; 0.90 for medulla). eGFR correlated moderately with cortical ({rho} = 0.46) and total parenchymal volume ({rho} = 0.45), and modestly with medullary volume ({rho} = 0.30). Similar associations were observed for other global measures, with the strongest correlation for cortical volume and tubular clearance ({rho} = 0.53). Side-specific volume fractions correlated with scintigraphy-derived split renal function ({rho} = 0.49-0.56; all p < 0.001). ConclusionAutomated CT-based renal subcompartment segmentation provides reproducible volumetric biomarkers within routine donor evaluation. Cortical volume performs comparably to total parenchymal volume and tracks split renal function at the cohort level, suggesting potential utility in donor assessment.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Evidences of histologic Thrombotic Microangiopathy and the impact in renal outcomes of patients with IgA nephropathy 94%
- Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts 93%
- Discoidan Domain Receptor 1 (DDR1) Tyrosine Kinase is Upregulated in PKD Kidneys But Does Not Play a Role in the Pathogenesis of Polycystic Kidney Disease 93%
Similar papers in this journal
- Intravital imaging of real-time endogenous actin dysregulation in proximal and distal tubules at the onset of severe ischemia-reperfusion injury 93%
- Glomerular spatial transcriptomics of IgA nephropathy according to the presence of mesangial proliferation 93%
- Gucy1α1 specifically marks kidney, heart, lung and liver fibroblasts 93%
Similar papers in this journal
- Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine 96%
- Nephron Number and Kidney Outcomes in IgA Nephropathy: A Retrospective Cohort Study 95%
- Packed Red Blood Cell and Whole Blood Perfusates during an Ex-vivo Normothermic Perfusion for Assessment of High-Risk Donor Kidneys 95%
Similar papers in this journal
Similar papers in this journal
- Assessing GPT-4 Multimodal Performance in Radiological Image Analysis 88%
- Evaluating Large Language Model-Generated Brain MRI Protocols: Performance of GPT4o, o3-mini, DeepSeek-R1 and Qwen2.5-72B 86%
- Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks 86%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.