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A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease
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 ADPK...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610271/ https://www.ncbi.nlm.nih.gov/pubmed/30666443 http://dx.doi.org/10.1007/s00330-018-5918-9 |
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author | Simms, Roslyn J. Doshi, Trushali Metherall, Peter Ryan, Desmond Wright, Peter Gruel, Nicolas van Gastel, Maatje D. A. Gansevoort, Ron T. Tindale, Wendy Ong, Albert C. M. |
author_facet | Simms, Roslyn J. Doshi, Trushali Metherall, Peter Ryan, Desmond Wright, Peter Gruel, Nicolas van Gastel, Maatje D. A. Gansevoort, Ron T. Tindale, Wendy Ong, Albert C. M. |
author_sort | Simms, Roslyn J. |
collection | PubMed |
description | 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 m(2)) 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-018-5918-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6610271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-66102712019-07-19 A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease Simms, Roslyn J. Doshi, Trushali Metherall, Peter Ryan, Desmond Wright, Peter Gruel, Nicolas van Gastel, Maatje D. A. Gansevoort, Ron T. Tindale, Wendy Ong, Albert C. M. Eur Radiol Magnetic Resonance 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 m(2)) 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-018-5918-9) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2019-01-21 2019 /pmc/articles/PMC6610271/ /pubmed/30666443 http://dx.doi.org/10.1007/s00330-018-5918-9 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Magnetic Resonance Simms, Roslyn J. Doshi, Trushali Metherall, Peter Ryan, Desmond Wright, Peter Gruel, Nicolas van Gastel, Maatje D. A. Gansevoort, Ron T. Tindale, Wendy Ong, Albert C. M. A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease |
title | A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease |
title_full | A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease |
title_fullStr | A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease |
title_full_unstemmed | A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease |
title_short | A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease |
title_sort | rapid high-performance semi-automated tool to measure total kidney volume from mri in autosomal dominant polycystic kidney disease |
topic | Magnetic Resonance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610271/ https://www.ncbi.nlm.nih.gov/pubmed/30666443 http://dx.doi.org/10.1007/s00330-018-5918-9 |
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