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European Radiology
Published in: European Radiology 8/2019

Open Access 01-08-2019 | Polycystic Kidney Disease | Magnetic Resonance

A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease

Authors: Roslyn J. Simms, Trushali Doshi, Peter Metherall, Desmond Ryan, Peter Wright, Nicolas Gruel, Maatje D. A. van Gastel, Ron T. Gansevoort, Wendy Tindale, Albert C. M. Ong

Published in: European Radiology | Issue 8/2019

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Abstract

Objectives

To develop a high-performance, rapid semi-automated method (Sheffield TKV Tool) for measuring total kidney volume (TKV) from magnetic resonance images (MRI) in patients with autosomal dominant polycystic kidney disease (ADPKD).

Methods

TKV was initially measured in 61 patients with ADPKD using the Sheffield TKV Tool and its performance compared to manual segmentation and other published methods (ellipsoidal, mid-slice, MIROS). It was then validated using an external dataset of MRI scans from 65 patients with ADPKD.

Results

Sixty-one patients (mean age 45 ± 14 years, baseline eGFR 76 ± 32 ml/min/1.73 m2) with ADPKD had a wide range of TKV (258–3680 ml) measured manually. The Sheffield TKV Tool was highly accurate (mean volume error 0.5 ± 5.3% for right kidney, − 0.7 ± 5.5% for left kidney), reproducible (intra-operator variability − 0.2 ± 1.3%; inter-operator variability 1.1 ± 2.9%) and outperformed published methods. It took less than 6 min to execute and performed consistently with high accuracy in an external MRI dataset of T2-weighted sequences with TKV acquired using three different scanners and measured using a different segmentation methodology (mean volume error was 3.45 ± 3.96%, n = 65).

Conclusions

The Sheffield TKV Tool is operator friendly, requiring minimal user interaction to rapidly, accurately and reproducibly measure TKV in this, the largest reported unselected European patient cohort with ADPKD. It is more accurate than estimating equations and its accuracy is maintained at larger kidney volumes than previously reported with other semi-automated methods. It is free to use, can run as an independent executable and will accelerate the application of TKV as a prognostic biomarker for ADPKD into clinical practice.

Key Points

• This new semi-automated method (Sheffield TKV Tool) to measure total kidney volume (TKV) will facilitate the routine clinical assessment of patients with ADPKD.
• Measuring TKV manually is time consuming and laborious.
• TKV is a prognostic indicator in ADPKD and the only imaging biomarker approved by the FDA and EMA.
Appendix
Available only for authorised users
Literature
1.
Schrier RW (2006) Optimal care of autosomal dominant polycystic kidney disease patients. Nephrology (Carlton) 11(2):124–130CrossRef
2.
Ong AC, Devuyst O, Knebelmann B, Walz G, ERA-EDTA Working Group for Inherited Kidney Diseases (2015) Autosomal dominant polycystic kidney disease: the changing face of clinical management. Lancet 385(9981):1993–2002CrossRefPubMed
3.
Grantham JJ, Mulamalla S, Swenson-Fields KI (2011) Why kidneys fail in autosomal dominant polycystic kidney disease. Nat Rev Nephrol 7(10):556–566CrossRefPubMed
4.
Perrone RD, Mouksassi MS, Romero K et al (2017) Total kidney volume is a prognostic biomarker of renal function decline and progression to end-stage renal disease in patients with autosomal dominant polycystic kidney disease. Kidney Int Rep 2(3):442–450
5.
Chapman AB, Bost JE, Torres VE et al (2012) Kidney volume and functional outcomes in autosomal dominant polycystic kidney disease. Clin J Am Soc Nephrol 7(3):479–486
6.
Irazabal MV, Rangel LJ, Bergstralh EJ et al (2015) Imaging classification of autosomal dominant polycystic kidney disease: a simple model for selecting patients for clinical trials. J Am Soc Nephrol 26(1):160–172CrossRefPubMed
7.
8.
Gansevoort RT, Arici M, Benzing T et al (2016) Recommendations for the use of tolvaptan in autosomal dominant polycystic kidney disease: a position statement on behalf of the ERA-EDTA Working Groups on Inherited Kidney Disorders and European Renal Best Practice. Nephrol Dial Transplant 31(3):337–348CrossRefPubMedPubMedCentral
9.
Spithoven EM, van Gastel MD, Messchendorp AL (2015) Estimation of total kidney volume in autosomal dominant polycystic kidney disease. Am J Kidney Dis 66(5):792–801CrossRefPubMed
10.
Bae KT, Commean PK, Lee J (2000) Volumetric measurement of renal cysts and parenchyma using MRI: phantoms and patients with polycystic kidney disease. J Comput Assist Tomogr 24(4):614–619CrossRefPubMed
11.
Zollner FG, Svarstad E, Munthe-Kaas AZ, Schad LR, Lundervold A, Rørvik J (2012) Assessment of kidney volumes from MRI: acquisition and segmentation techniques. AJR Am J Roentgenol 199(5):1060–1069CrossRefPubMed
12.
Kim Y, Ge Y, Tao C et al (2016) Automated segmentation of kidneys from MR images in patients with autosomal dominant polycystic kidney disease. Clin J Am Soc Nephrol 11(4):576–584CrossRefPubMedPubMedCentral
13.
Sharma K, Rupprecht C, Caroli A et al (2017) Automatic segmentation of kidneys using deep learning for total kidney volume quantification in autosomal dominant polycystic kidney disease. Sci Rep 7(1):2049 p. 1–10CrossRefPubMedPubMedCentral
14.
Kline TL, Korfiatis P, Edwards ME et al (2017) Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys. J Digit Imaging 30(4):442–448
15.
Magistroni R, Corsi C, Martí T, Torra R (2018) A review of the imaging techniques for measuring kidney and cyst volume in establishing autosomal dominant polycystic kidney disease progression. Am J Nephrol 48(1):67–78
16.
Sharma K, Caroli A, Quach LV et al (2017) Kidney volume measurement methods for clinical studies on autosomal dominant polycystic kidney disease. PLoS One 12(5):e0178488CrossRefPubMedPubMedCentral
17.
18.
Kline TL, Edwards ME, Korfiatis P, Akkus Z, Torres VE, Erickson BJ (2016) Semiautomated segmentation of polycystic kidneys in T2-weighted MR images. AJR Am J Roentgenol 207(3):605–613CrossRefPubMedPubMedCentral
19.
Salvado O, Hillenbrand C, Zhang S, Wilson DL (2006) Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization. IEEE Trans Med Imaging 25(5):539–552
20.
Zhang Y, Shark L, Moore CJ, Matuszewski BJ (2008) Medical image segmentation using new hybrid level-set method. Fifth International Conference Biomedical Visualization: Information Visualization in Medical and Biomedical Informatics (MEDIVIS) 00:71–76
21.
King BF, Reed JE, Bergstralh EJ, Sheedy PF 2nd, Torres VE (2000) Quantification and longitudinal trends of kidney, renal cyst, and renal parenchyma volumes in autosomal dominant polycystic kidney disease. J Am Soc Nephrol 11:1505–1511
22.
Bae KT, Tao C, Wang J et al (2013) Novel approach to estimate kidney and cyst volumes using mid-slice magnetic resonance images in polycystic kidney disease. Am J Nephrol 38:333–341CrossRefPubMed
23.
Meijer E, Drenth JP, d'Agnolo H et al (2014) Rationale and design of the DIPAK 1 study: a randomized controlled clinical trial assessing the efficacy of lanreotide to halt disease progression in autosomal dominant polycystic kidney disease. Am J Kidney Dis 63(3):446–455
24.
25.
Zou KH, Warfield SK, Bharatha A et al (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11(2):178–189CrossRefPubMedPubMedCentral
26.
27.
Bae K, Park B, Sun H et al (2013) Segmentation of individual renal cysts from MR images in patients with autosomal dominant polycystic kidney disease. Clin J Am Soc Nephrol 8(7):1089–1097CrossRefPubMedPubMedCentral
28.
Turco D, Severi S, Mignani R, Aiello V, Magistroni R, Corsi C (2015) Reliability of total renal volume computation in polycystic kidney disease from magnetic resonance imaging. Acad Radiol 22(11):1376–1384CrossRefPubMed
29.
van Gastel MDA, Messchendorp AL, Kappert P et al (2018) T1 vs T2 weighted magnetic resonance imaging to assess total kidney volume in patients with autosomal dominant polycystic kidney disease. Abdom Radiol (NY) 43(5):1215–1222
30.
Kline TL, Korfiatis P, Edwards ME et al (2016) Automatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression. Nephrol Dial Transplant 31(2):241–248PubMed
Metadata
Title
A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease
Authors
Roslyn J. Simms
Trushali Doshi
Peter Metherall
Desmond Ryan
Peter Wright
Nicolas Gruel
Maatje D. A. van Gastel
Ron T. Gansevoort
Wendy Tindale
Albert C. M. Ong
Publication date
01-08-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5918-9

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