Evidence map›Paper›PMID 35058878›Full record

ArticleFrontiers in endocrinology2021

Texture Analysis Using CT and Chemical Shift Encoding-Based Water-Fat MRI Can Improve Differentiation Between Patients With and Without Osteoporotic Vertebral Fractures.

Nico Sollmann, Edoardo A Becherucci, Christof Boehm, Malek El Husseini, Stefan Ruschke, Egon Burian, Jan S Kirschke, Thomas M Link, Karupppasamy Subburaj, Dimitrios C Karampinos and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
3.1field-weighted citation impact, top 8% 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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024
    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

13 authors at 5 institutions in 3 countries.

Nico SollmannDepartment of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany.
Edoardo A BecherucciDepartment of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Christof BoehmDepartment of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Malek El HusseiniDepartment of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Stefan RuschkeDepartment of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Egon BurianDepartment of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Jan S KirschkeDepartment of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Thomas M LinkDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States.
Karupppasamy SubburajEngineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore, Singapore.
Dimitrios C KarampinosDepartment of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Roland KrugDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States.
Thomas BaumDepartment of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Michael DieckmeyerDepartment of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Klinikum rechts der Isar · DETechnical University of Munich · DEUniversity of California, San Francisco · USChangi General Hospital · SGUniversity of California System · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Osteoporosis is a highly prevalent skeletal disease that frequently entails vertebral fractures. Areal bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the reference standard, but has well-known limitations. Texture analysis can provide surrogate markers of tissue microstructure based on computed tomography (CT) or magnetic resonance imaging (MRI) data of the spine, thus potentially improving fracture risk estimation beyond areal BMD. However, it is largely unknown whether MRI-derived texture analysis can predict volumetric BMD (vBMD), or whether a model incorporating texture analysis based on CT and MRI may be capable of differentiating between patients with and without osteoporotic vertebral fractures. Materials and Methods: Twenty-six patients (15 females, median age: 73 years, 11 patients showing at least one osteoporotic vertebral fracture) who had CT and 3-Tesla chemical shift encoding-based water-fat MRI (CSE-MRI) available were analyzed. In total, 171 vertebral bodies of the thoracolumbar spine were segmented using an automatic convolutional neural network (CNN)-based framework, followed by extraction of integral and trabecular vBMD using CT data. For CSE-MRI, manual segmentation of vertebral bodies and consecutive extraction of the mean proton density fat fraction (PDFF) and T2* was performed. First-order, second-order, and higher-order texture features were derived from texture analysis using CT and CSE-MRI data. Stepwise multivariate linear regression models were computed using integral vBMD and fracture status as dependent variables. Results: Patients with osteoporotic vertebral fractures showed significantly lower integral and trabecular vBMD when compared to patients without fractures (p<0.001). For the model with integral vBMD as the dependent variable, T2* combined with three PDFF-based texture features explained 40% of the variance (adjusted R Conclusion: Using texture analysis for spine CT and CSE-MRI can facilitate the differentiation between patients with and without osteoporotic vertebral fractures, implicating that future fracture prediction in osteoporosis may be improved.

Indexed as

Bone DensityAdultAgedAged, 80 and overCancellous BoneFemaleHumansImage Processing, Computer-AssistedLumbar VertebraeMagnetic Resonance ImagingMaleMiddle AgedOsteoporosisOsteoporotic FracturesSpinal FracturesThoracic Vertebraebone mineral densityconvolutional neural networkopportunistic imagingosteoporosisproton density fat fractiontexture analysisvertebral fracture

Identifiers

PMID35058878
PMCPMC8763669
OpenAlexW4205818862

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.