title: Novel methodology to evaluate renal cysts in polycystic kidney disease.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pa., USA.
authors.forename: Kyongtae T
authors.initials: KT
authors.lastname: Bae
authors.forename: Hongliang
authors.initials: H
authors.lastname: Sun
authors.forename: June Goo
authors.initials: JG
authors.lastname: Lee
authors.forename: Kyungsoo
authors.initials: K
authors.lastname: Bae
authors.forename: Jinhong
authors.initials: J
authors.lastname: Wang
authors.forename: Cheng
authors.initials: C
authors.lastname: Tao
authors.forename: Arlene B
authors.initials: AB
authors.lastname: Chapman
authors.forename: Vicente E
authors.initials: VE
authors.lastname: Torres
authors.forename: Jared J
authors.initials: JJ
authors.lastname: Grantham
authors.forename: Michal
authors.initials: M
authors.lastname: Mrug
authors.forename: William M
authors.initials: WM
authors.lastname: Bennett
authors.forename: Michael F
authors.initials: MF
authors.lastname: Flessner
authors.forename: Doug P
authors.initials: DP
authors.lastname: Landsittel
mesh_terms: D000293:Adolescent; D000328:Adult; D003560:Cysts / Q000175:diagnosis* / Q000473:pathology*; D018450:Disease Progression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007668:Kidney / Q000473:pathology* / Q000503:physiopathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D016891:Polycystic Kidney, Autosomal Dominant / Q000175:diagnosis* / Q000473:pathology*; D055815:Young Adult