Novel Methodology to Evaluate Renal Cysts in Polycystic Kidney Disease

• Type of Material: Article
• Authors: Kyontae T Bae, University of Pittsburgh; Hongliang Sun, University of Pittsburgh; June Goo Lee, University of Pittsburgh; Kyungsoo Bae, Gyeongsang National University; Jinhong Wang, University of Pittsburgh; Cheng Tao, University of Pittsburgh; Arlene B Chapman, Emory University; Vicente E Torres, Mayo College of Medicine; Jared J Grantham, University of Kansas; Michal Mrug, University of Alabama Birmingham; William M Bennett, Legacy Good Samaritan Hospital; Michael F Flessner, National Institute of Diabetes and Digestive and Kidney Diseases; Doug P Landsittel, University of Pittsburgh
• Language: English
• Date: 2014-01-01
• Publisher: Karger Publishers
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 S. Karger AG, Basel.
• Final Published Version (URL): http://dx.doi.org/10.1159/000358604
• Title of Journal or Parent Work: American Journal of Nephrology
• ISSN: 0250-8095
• Volume: 39
• Issue: 3
• Start Page: 210
• End Page: 217
• Grant/Funding Information: The CRISP study is supported by cooperative agreements from the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (DK056943, DK056956, DK056957, DK056961); and by the National Center for Research Resources General Clinical Research Centers at each institution (RR000039, Emory University; RR00585, Mayo College of Medicine; RR23940, Kansas University Medical Center; RR000032, University of Alabama at Birmingham); and the National Center for Research Resources Clinical and Translational Science Awards at each institution (RR025008, Emory; RR024150, Mayo College of Medicine; RR033179, Kansas University Medical Center; RR025777 and UL1TR000165, University of Alabama at Birmingham; RR024153 and UL1TR000005, University of Pittsburgh School of Medicine).
• Keywords: Kidney; VOLUME; SEGMENTATION; Polycystic kidney diseases; Renal cysts; Segmentation; CONSORTIUM; GROWTH; Urology & Nephrology; Magnetic resonance imaging; Science & Technology; IMAGES; MRI; Life Sciences & Biomedicine
• Research Categories: Health Sciences, Radiology; Health Sciences, Medicine and Surgery

Abstract

Aim: To develop and assess a semiautomated method for segmenting and counting individual renal cysts from mid-slice MR images in patients with autosomal dominant polycystic kidney disease (ADPKD). Methods: A semiautomated method was developed to segment and count individual renal cysts from mid-slice MR images in 241 subjects with ADPKD from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. For each subject, a mid-slice MR image was selected from each set of coronal T2-weighted MR images covering the entire kidney. The selected mid-slice image was processed with the semiautomated method to segment and count individual renal cysts. The number of cysts from the mid-slice image of each kidney was also measured by manual counting. The level of agreement between the semiautomated and manual cyst counts was compared using intraclass correlation (ICC) and a Bland-Altman plot. Results: Individual renal cysts were successfully segmented using the semiautomated method in all 241 cases. The number of cysts in each kidney measured with the semiautomated and manual counting methods correlated well (ICC = 0.96 for the right or left kidney), with a small average difference (-0.52, with higher semiautomated counts, for the right kidney, and 0.13, with higher manual counts, for the left kidney) in the semiautomated method. However, there was substantial variation in a small number of subjects; 6 of 241 participants (2.5%) had a difference in the total cyst count of more than 15. Conclusion: We have developed a semiautomated method to segment individual renal cysts from mid-slice MR images in ADPKD kidneys as a quantitative indicator of characterization and disease progression of ADPKD.