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Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures.

Materials and methods

Standard CT scans of 58 patients with insufficiency fractures of the spine, performed between 2006 and 2013, were analyzed retrospectively. Every included patient had at least two CT scans. Intact vertebrae in a first scan that either fractured (“unstable”) or remained intact (“stable”) in the consecutive scan were manually segmented on mid-sagittal reformations. TA features for all vertebrae were extracted using open-source software (MaZda). In a paired control study, all vertebrae of the study cohort “cases” and matched controls were classified using ROC analysis of Hounsfield unit (HU) measurements and supervised ML techniques. In a within-subject vertebra comparison, vertebrae of the cases were classified into “unstable” and “stable” using identical techniques.

Results

One hundred twenty vertebrae were included. Classification of cases/controls using ROC analysis of HU measurements showed an AUC of 0.83 (95% confidence interval [CI], 0.77–0.88), and ML-based classification showed an AUC of 0.97 (CI, 0.97–0.98). Classification of unstable/stable vertebrae using ROC analysis showed an AUC of 0.52 (CI, 0.42–0.63), and ML-based classification showed an AUC of 0.64 (CI, 0.61–0.67).

Conclusion

TA combined with ML allows to identifying patients who will suffer from vertebral insufficiency fractures in standard CT scans with high accuracy. However, identification of single vertebra at risk remains challenging.

Key Points

• Bone texture analysis combined with machine learning allows to identify patients at risk for vertebral body insufficiency fractures on standard CT scans with high accuracy.

• Compared to mere Hounsfield unit measurements on CT scans, application of bone texture analysis combined with machine learning improve fracture risk prediction.

• This analysis has the potential to identify vertebrae at risk for insufficiency fracture and may thus increase diagnostic value of standard CT scans.

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Abbreviations

ANN:

Artificial neural networks

BMD:

Bone mineral density

CCC:

Concordance correlation coefficient

DXA:

Dual-energy X-ray absorptiometry

FEA:

Finite element analysis

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

HR-pQCT:

High-resolution peripheral quantitative computed tomography

IH:

Image histogram

ML:

Machine learning

MLP:

Multi-layer perceptron

RF:

Random forest

ROI:

Region of interest

SVM:

Support vector machine

TA:

Texture analysis

TLJ:

Thoracic-lumbar junction

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Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland

    Urs J. Muehlematter, Manoj Mannil, Anton S. Becker, Tim Finkenstaedt & Roman Guggenberger

  2. University Hospital of Zurich, Zurich, Switzerland

    Kerstin N. Vokinger

  3. University of Zurich, Zurich, Switzerland

    Kerstin N. Vokinger

  4. Department of Trauma, University Hospital Zurich, Zurich, Switzerland

    Georg Osterhoff

  5. Department of Radiology, University Hospital Balgrist, University of Zurich, Zurich, Switzerland

    Michael A. Fischer

Authors
  1. Urs J. Muehlematter
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  2. Manoj Mannil
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  3. Anton S. Becker
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  4. Kerstin N. Vokinger
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  5. Tim Finkenstaedt
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  7. Michael A. Fischer
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  8. Roman Guggenberger
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Corresponding author

Correspondence to Urs J. Muehlematter.

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Guarantor

The scientific guarantor of this publication is Roman Guggenberger.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

The control cohort used in the present study (58 patients) is part of a study population of a previously published study establishing normative values for CT-based texture analysis of vertebral bodies. Mannil M, Eberhard M, Becker AS, et al (2017) Normative values for CT-based texture analysis of vertebral bodies in dual X-ray absorptiometry-confirmed, normally mineralized subjects. Skeletal Radiology 46:1541–1551. https://doi.org/10.1007/s00256-017-2728-0).

Methodology

• retrospective

• case-control study

• performed at one institution

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Muehlematter, U.J., Mannil, M., Becker, A.S. et al. Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning. Eur Radiol 29, 2207–2217 (2019). https://doi.org/10.1007/s00330-018-5846-8

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  • DOI: https://doi.org/10.1007/s00330-018-5846-8

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