Novel methodology to evaluate renal cysts in polycystic kidney disease.

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.

journal_name

Am J Nephrol

authors

Bae KT,Sun H,Lee JG,Bae K,Wang J,Tao C,Chapman AB,Torres VE,Grantham JJ,Mrug M,Bennett WM,Flessner MF,Landsittel DP,Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease.

doi

10.1159/000358604

subject

Has Abstract

pub_date

2014-01-01 00:00:00

pages

210-7

issue

3

eissn

0250-8095

issn

1421-9670

pii

000358604

journal_volume

39

pub_type

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