Review
Version 1
Preserved in Portico This version is not peer-reviewed
Imaging in ADPKD: Beyond Total Kidney Volume
Version 1
: Received: 31 May 2023 / Approved: 31 May 2023 / Online: 31 May 2023 (13:18:14 CEST)
A peer-reviewed article of this Preprint also exists.
Caroli, A.; Kline, T.L. Abdominal Imaging in ADPKD: Beyond Total Kidney Volume. J. Clin. Med. 2023, 12, 5133. Caroli, A.; Kline, T.L. Abdominal Imaging in ADPKD: Beyond Total Kidney Volume. J. Clin. Med. 2023, 12, 5133.
Abstract
In the context of autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) plays a key role as biomarker of disease progression and response to therapy and has been recently recognized as enrichment biomarker and possible surrogate endpoint. Several imaging modalities and methods are available to calculate TKV, and the choice depends on the purpose of use. Technological advancements have made it possible to accurately assess cyst burden, that can be crucial to assess the disease state and help identifying rapid progressors. Moreover, the development of automated algorithms has increased the efficiency of total kidney and cyst volume measurements. Beyond total kidney and cyst volume, the quantification and characterization of non-cystic kidney tissue show potential to early stratify ADPKD patients, monitor disease progression, and possibly predict renal function loss. A broad spectrum of kid-ney MRI techniques are available to characterize kidney tissue, showing promise to non-invasively pick up early signs of ADPKD progression. Radiomics has been used to extract tex-tural features from ADPKD images, providing valuable information about the heterogeneity of the cystic and non-cystic components. This review provides an overview of ADPKD imaging bi-omarkers, focusing on quantification methods, potential, and necessary steps towards successful translation to clinical practice.
Keywords
autosomal dominant polycystic kidney disease; total kidney volume; cyst volume; non-cystic tissue; magnetic resonance imaging; segmentation; artificial intelligence
Subject
Medicine and Pharmacology, Urology and Nephrology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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