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Article: Novel approach to estimate kidney and cyst volumes using mid-slice magnetic resonance images in polycystic kidney disease

TitleNovel approach to estimate kidney and cyst volumes using mid-slice magnetic resonance images in polycystic kidney disease
Authors
KeywordsKidney
Kidney volume
Magnetic resonance imaging
Polycystic kidney disease
Renal cysts
Issue Date2013
Citation
American Journal of Nephrology, 2013, v. 38, n. 4, p. 333-341 How to Cite?
AbstractObjective: To evaluate whether kidney and cyst volumes can be accurately estimated based on limited area measurements from magnetic resonance (MR) images of patients with autosomal dominant polycystic kidney disease (ADPKD). Materials and Methods: MR coronal images of 178 ADPKD participants from the Consortium for Radiologic Imaging Studies of ADPKD (CRISP) were analyzed. For each MR image slice, we measured kidney and renal cyst areas using stereology and region-based thresholding methods, respectively. The kidney and cyst 'observed' volumes were calculated by summing up the area measurements of all the slices covering the kidney. To estimate the volume, we selected a coronal mid-slice in each kidney and multiplied its area by the total number of slices ('PANK2' for kidney and 'PANC2' for cyst). We then compared the kidney and cyst volumes predicted from PANK2 and PANC2, respectively, to the corresponding observed volumes, using a linear regression analysis. Results: The kidney volume predicted from PANK2 correlated extremely well with the observed kidney volume (R2 = 0.994 for the right kidney and 0.991 for the left kidney). The linear regression coefficient multiplier to PANK2 that best fit the kidney volume was 0.637 (95% CI: 0.629-0.644) for the right kidney and 0.624 (95% CI: 0.616-0.633) for the left kidney. The correlation between the cyst volume predicted from PANC2 and the observed cyst volume was also very high (R 2 = 0.984 for the right kidney and 0.967 for the left kidney). The least squares linear regression coefficient for PANC2 was 0.637 (95% CI: 0.624-0.649) for the right kidney and 0.608 (95% CI: 0.591-0.625) for the left kidney. Conclusion: Kidney and cyst volumes can be closely approximated by multiplying the product of the mid-slice area measurement and the total number of slices in the coronal MR images of ADPKD kidneys by 0.61-0.64. This information will help save processing time needed to estimate total kidney and cyst volumes of ADPKD kidneys. © 2013 S. Karger AG, Basel.
Persistent Identifierhttp://hdl.handle.net/10722/316081
ISSN
2023 Impact Factor: 4.3
2023 SCImago Journal Rankings: 1.218
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorBae, Kyongtae T.-
dc.contributor.authorTao, Cheng-
dc.contributor.authorWang, Jinhong-
dc.contributor.authorKaya, Diana-
dc.contributor.authorWu, Zhiyuan-
dc.contributor.authorBae, Junu T.-
dc.contributor.authorChapman, Arlene B.-
dc.contributor.authorTorres, Vicente E.-
dc.contributor.authorGrantham, Jared J.-
dc.contributor.authorMrug, Michal-
dc.contributor.authorBennett, William M.-
dc.contributor.authorFlessner, Michael F.-
dc.contributor.authorLandsittel, Doug P.-
dc.date.accessioned2022-08-24T15:49:10Z-
dc.date.available2022-08-24T15:49:10Z-
dc.date.issued2013-
dc.identifier.citationAmerican Journal of Nephrology, 2013, v. 38, n. 4, p. 333-341-
dc.identifier.issn0250-8095-
dc.identifier.urihttp://hdl.handle.net/10722/316081-
dc.description.abstractObjective: To evaluate whether kidney and cyst volumes can be accurately estimated based on limited area measurements from magnetic resonance (MR) images of patients with autosomal dominant polycystic kidney disease (ADPKD). Materials and Methods: MR coronal images of 178 ADPKD participants from the Consortium for Radiologic Imaging Studies of ADPKD (CRISP) were analyzed. For each MR image slice, we measured kidney and renal cyst areas using stereology and region-based thresholding methods, respectively. The kidney and cyst 'observed' volumes were calculated by summing up the area measurements of all the slices covering the kidney. To estimate the volume, we selected a coronal mid-slice in each kidney and multiplied its area by the total number of slices ('PANK2' for kidney and 'PANC2' for cyst). We then compared the kidney and cyst volumes predicted from PANK2 and PANC2, respectively, to the corresponding observed volumes, using a linear regression analysis. Results: The kidney volume predicted from PANK2 correlated extremely well with the observed kidney volume (R2 = 0.994 for the right kidney and 0.991 for the left kidney). The linear regression coefficient multiplier to PANK2 that best fit the kidney volume was 0.637 (95% CI: 0.629-0.644) for the right kidney and 0.624 (95% CI: 0.616-0.633) for the left kidney. The correlation between the cyst volume predicted from PANC2 and the observed cyst volume was also very high (R 2 = 0.984 for the right kidney and 0.967 for the left kidney). The least squares linear regression coefficient for PANC2 was 0.637 (95% CI: 0.624-0.649) for the right kidney and 0.608 (95% CI: 0.591-0.625) for the left kidney. Conclusion: Kidney and cyst volumes can be closely approximated by multiplying the product of the mid-slice area measurement and the total number of slices in the coronal MR images of ADPKD kidneys by 0.61-0.64. This information will help save processing time needed to estimate total kidney and cyst volumes of ADPKD kidneys. © 2013 S. Karger AG, Basel.-
dc.languageeng-
dc.relation.ispartofAmerican Journal of Nephrology-
dc.subjectKidney-
dc.subjectKidney volume-
dc.subjectMagnetic resonance imaging-
dc.subjectPolycystic kidney disease-
dc.subjectRenal cysts-
dc.titleNovel approach to estimate kidney and cyst volumes using mid-slice magnetic resonance images in polycystic kidney disease-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1159/000355375-
dc.identifier.pmid24107679-
dc.identifier.scopuseid_2-s2.0-84885051606-
dc.identifier.volume38-
dc.identifier.issue4-
dc.identifier.spage333-
dc.identifier.epage341-
dc.identifier.eissn1421-9670-
dc.identifier.isiWOS:000326134100009-

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