Evidence map›Paper›PMID 30151641›Full record

ArticleEuropean radiology2019

Automated MR-based lung volume segmentation in population-based whole-body MR imaging: correlation with clinical characteristics, pulmonary function testing and obstructive lung disease.

Jan Mueller, Stefan Karrasch, Roberto Lorbeer, Tatyana Ivanovska, Andreas Pomschar, Wolfgang G Kunz, Ricarda von Krüchten, Annette Peters, Fabian Bamberg, Holger Schulz and 1 more

Open access · greenAbstract read
PubMed Publisher
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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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

11 authors at 7 institutions in 1 country.

Jan MuellerDepartment of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Stefan KarraschInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Roberto LorbeerDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany.
Tatyana IvanovskaDepartment of Computational Neuroscience, Computer Vision, Georg-August-University, Gottingen, Germany.
Andreas PomscharDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany.
Wolfgang G KunzDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany.
Ricarda von KrüchtenDepartment of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Annette PetersInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Fabian BambergDepartment of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany.
Holger SchulzInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Christopher L SchlettDepartment of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany. Christopher.Schlett@post.harvard.edu.
German Center for Lung Research · DEHeidelberg University · DELMU Klinikum · DELudwig-Maximilians-Universität München · DEHelmholtz Zentrum München · DEUniversity of Göttingen · DEUniversity of Tübingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWhole-body MR imaging is increasingly utilised; although for lung dedicated sequences are often not included, the chest is typically imaged. Our objective was to determine the clinical utility of lung volumes derived from non-dedicated MRI sequences in the population-based KORA-FF4 cohort study.

methods400 subjects (56.4 ± 9.2 years, 57.6% males) underwent whole-body MRI including a coronal T1-DIXON-VIBE sequence in inspiration breath-hold, originally acquired for fat quantification. Based on MRI, lung volumes were derived using an automated framework and related to common predictors, pulmonary function tests (PFT; spirometry and pulmonary gas exchange, n = 214) and obstructive lung disease.

resultsMRI-based lung volume was 4.0 ± 1.1 L, which was 64.8 ± 14.9% of predicted total lung capacity (TLC) and 124.4 ± 27.9% of functional residual capacity. In multivariate analysis, it was positively associated with age, male, current smoking and height. Among PFT indices, MRI-based lung volume correlated best with TLC, alveolar volume and residual volume (RV; r = 0.57 each), while it was negatively correlated to FEV

conclusionLung volume derived from non-dedicated whole-body MRI is independently associated with RV and FEV KEY POINTS: • Although whole-body MRI often does not include dedicated lung sequences, lung volume can be automatically derived using dedicated segmentation algorithms • Lung volume derived from whole-body MRI correlates with typical predictors and risk factors of respiratory function including smoking and represents about 65% of total lung capacity and 125% of the functional residual capacity • Lung volume derived from whole-body MRI is independently associated with residual volume and the ratio of forced expiratory volume in 1 s to forced vital capacity and may allow detection of obstructive lung disease.

Indexed as

Lung Volume MeasurementsMagnetic Resonance ImagingRespiratory Function TestsAgedAlgorithmsCase-Control StudiesFemaleForced Expiratory VolumeHumansImage Processing, Computer-AssistedLungMaleMiddle AgedProspective StudiesPulmonary Disease, Chronic ObstructiveResidual VolumeComputer-assisted image analysisMagnetic resonance imagingObstructive lung diseasePulmonary function testWhole-body imaging

Identifiers

PMID30151641
OpenAlexW2888940009

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.