Novel methodology to evaluate renal cysts in polycystic kidney disease

Kyongtae T. Bae, Hongliang Sun, June Goo Lee, Kyungsoo Bae, Jinhong Wang, Cheng Tao, Arlene B. Chapman, Vicente Torres, Jared J. Grantham, Michal Mrug, William M. Bennett, Michael F. Flessner, Doug P. Landsittel

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)210-217
Number of pages8
JournalAmerican Journal of Nephrology
Volume39
Issue number3
DOIs
StatePublished - 2014

Fingerprint

Polycystic Kidney Diseases
Cysts
Kidney
Autosomal Dominant Polycystic Kidney
Disease Progression

Keywords

  • Kidney
  • Magnetic resonance imaging
  • Polycystic kidney disease
  • Renal cysts
  • Segmentation

ASJC Scopus subject areas

  • Nephrology
  • Medicine(all)

Cite this

Bae, K. T., Sun, H., Lee, J. G., Bae, K., Wang, J., Tao, C., ... Landsittel, D. P. (2014). Novel methodology to evaluate renal cysts in polycystic kidney disease. American Journal of Nephrology, 39(3), 210-217. https://doi.org/10.1159/000358604

Novel methodology to evaluate renal cysts in polycystic kidney disease. / Bae, Kyongtae T.; Sun, Hongliang; Lee, June Goo; Bae, Kyungsoo; Wang, Jinhong; Tao, Cheng; Chapman, Arlene B.; Torres, Vicente; Grantham, Jared J.; Mrug, Michal; Bennett, William M.; Flessner, Michael F.; Landsittel, Doug P.

In: American Journal of Nephrology, Vol. 39, No. 3, 2014, p. 210-217.

Research output: Contribution to journalArticle

Bae, KT, Sun, H, Lee, JG, Bae, K, Wang, J, Tao, C, Chapman, AB, Torres, V, Grantham, JJ, Mrug, M, Bennett, WM, Flessner, MF & Landsittel, DP 2014, 'Novel methodology to evaluate renal cysts in polycystic kidney disease', American Journal of Nephrology, vol. 39, no. 3, pp. 210-217. https://doi.org/10.1159/000358604
Bae, Kyongtae T. ; Sun, Hongliang ; Lee, June Goo ; Bae, Kyungsoo ; Wang, Jinhong ; Tao, Cheng ; Chapman, Arlene B. ; Torres, Vicente ; Grantham, Jared J. ; Mrug, Michal ; Bennett, William M. ; Flessner, Michael F. ; Landsittel, Doug P. / Novel methodology to evaluate renal cysts in polycystic kidney disease. In: American Journal of Nephrology. 2014 ; Vol. 39, No. 3. pp. 210-217.
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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.",
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AU - Tao, Cheng

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AB - 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.

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