Deep Learning‐Based MRI Volumetry for Living Kidney Donor Assessment: A New Tool for Predicting Post‐Donation Renal Function
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.
Authors
- Stephan Kemmner (ORCID: https://orcid.org/0000-0002-1242-4914)
- Severin Jacobi (ORCID: https://orcid.org/0000-0003-1804-5090)
- Maximilian Scheifele
- Dominik Koch (ORCID: https://orcid.org/0000-0002-7409-0032)
- Dionysios Koliogiannis (ORCID: https://orcid.org/0000-0002-8001-8547)
- Bernhard Willibald Renz (ORCID: https://orcid.org/0000-0001-9574-8051)
- Matthias Ilmer (ORCID: https://orcid.org/0000-0001-9597-1368)
- Markus Guba (ORCID: https://orcid.org/0000-0002-7778-8401)
- Jens Werner (ORCID: https://orcid.org/0000-0001-7813-2482)
- Malte Schirren
- Tobias Seibt
- Felix O. Hofmann (ORCID: https://orcid.org/0000-0002-6913-2429)
- Manfred Stangl
- Dimitrios Trompoukis
Institutions
- Mayo Clinic (US)
- LMU Klinikum (DE)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Clinical Transplantation
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1111/ctr.70338
- Citations
- 1
- Primary Topic
- Organ Donation and Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 1.88