Evidence map›Paper›PMID 30666443›Full record

ArticleEuropean radiology2019

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

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

Open access · hybridAbstract readComparative StudyEvaluation StudyMulticenter Study
In one paragraph

Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  3. MRI of kidney size matters.Magma (New York, N.Y.) · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 2 countries.

Roslyn J SimmsKidney Genetics Group, Academic Unit of Nephrology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.
Trushali DoshiKidney Genetics Group, Academic Unit of Nephrology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.
Peter MetherallInstitute for in silico Medicine, University of Sheffield, Sheffield, UK.
Desmond RyanKidney Genetics Group, Academic Unit of Nephrology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.
Peter WrightMedical Imaging and Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
Nicolas GruelInstitute for in silico Medicine, University of Sheffield, Sheffield, UK.
Maatje D A van GastelDepartment of Nephrology, University Medical Center Groningen, Groningen, the Netherlands.
Ron T GansevoortDepartment of Nephrology, University Medical Center Groningen, Groningen, the Netherlands.
Wendy TindaleInstitute for in silico Medicine, University of Sheffield, Sheffield, UK.
Albert C M OngKidney Genetics Group, Academic Unit of Nephrology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK. a.ong@sheffield.ac.uk.ORCID http://orcid.org/0000-0002-7211-5400
Sheffield Teaching Hospitals NHS Foundation Trust · GBUniversity Medical Center Groningen · NLUniversity of Sheffield · GBInsigneo · GBSheffield Kidney Institute · GB

Funding

Medical Research Council MC_PC_15034
6 · The paper itself

Abstract

objectivesTo 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).

methodsTKV 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.

resultsSixty-one patients (mean age 45 ± 14 years, baseline eGFR 76 ± 32 ml/min/1.73 m

conclusionsThe 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.

Indexed as

AdultAgedCross-Sectional StudiesDisease ProgressionFemaleGlomerular Filtration RateHumansKidneyMagnetic Resonance ImagingMaleMiddle AgedOrgan SizePolycystic Kidney, Autosomal DominantPrognosisRetrospective StudiesYoung AdultAutosomal dominant polycystic kidney diseaseKidneysMagnetic resonance imagingPolycystic kidney diseases

Identifiers

PMID30666443
PMCPMC6610271
OpenAlexW2911816654

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.