Evidence map›Paper›PMID 26330562›Full record

ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2016

Automatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression.

Timothy L Kline, Panagiotis Korfiatis, Marie E Edwards, Joshua D Warner, Maria V Irazabal, Bernard F King, Vicente E Torres, Bradley J Erickson

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In one paragraph

Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

38 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Timothy L KlineDepartment of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA.
Panagiotis KorfiatisDepartment of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA.
Marie E EdwardsDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA.
Joshua D WarnerDepartment of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA.
Maria V IrazabalDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA.
Bernard F KingDepartment of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA.
Vicente E TorresDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA.
Bradley J EricksonDepartment of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA.

Funding

Pilot and FeasibilityP30DK090728 · NIDDK · MAYO CLINIC ROCHESTER · PI ROMERO, MICHAEL F. · 2010 to 2019
$11.8M
Objective decision support environment for clinical trialsU01CA160045 · NCI · MAYO CLINIC ROCHESTER · PI ERICKSON, BRADLEY J. · 2012 to 2016
$2.6M
NCI NIH HHS U01 CA160045NIDDK NIH HHS P30 DK090728
6 · The paper itself

Abstract

backgroundRenal imaging examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) patients. TKV has become the gold-standard image biomarker for ADPKD progression at early stages of the disease and is used in clinical trials to characterize treatment efficacy. Automated methods to segment the kidneys and measure TKV are desirable because of the long time requirement for manual approaches such as stereology or planimetry tracings. However, ADPKD kidney segmentation is complicated by a number of factors, including irregular kidney shapes and variable tissue signal at the kidney borders.

methodsWe describe an image processing approach that overcomes these problems by using a baseline segmentation initialization to provide automatic segmentation of follow-up scans obtained years apart. We validated our approach using 20 patients with complete baseline and follow-up T1-weighted magnetic resonance images. Both manual tracing and stereology were used to calculate TKV, with two observers performing manual tracings and one observer performing repeat tracings. Linear correlation and Bland-Altman analysis were performed to compare the different approaches.

resultsOur automated approach measured TKV at a level of accuracy (mean difference ± standard error = 0.99 ± 0.79%) on par with both intraobserver (0.77 ± 0.46%) and interobserver variability (1.34 ± 0.70%) of manual tracings. All approaches had excellent agreement and compared favorably with ground-truth manual tracing with interobserver, stereological and automated approaches having 95% confidence intervals ∼ ± 100 mL.

conclusionsOur method enables fast, cost-effective and reproducible quantification of ADPKD progression that will facilitate and lower the costs of clinical trials in ADPKD and other disorders requiring accurate, longitudinal kidney quantification. In addition, it will hasten the routine use of TKV as a prognostic biomarker in ADPKD.

Indexed as

AdultDisease ProgressionFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedKidneyMagnetic Resonance ImagingMaleMiddle AgedMonitoring, PhysiologicOrgan SizePolycystic Kidney, Autosomal DominantPrognosisROC Curveautosomal dominant polycystic kidney diseasemagnetic resonance imagingplanimetryregistrationsegmentationstereology

Identifiers

PMID26330562
PMCPMC4725388

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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.