European Radiology

, Volume 29, Issue 5, pp 2207–2217 | Cite as

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

  • Urs J. MuehlematterEmail author
  • Manoj Mannil
  • Anton S. Becker
  • Kerstin N. Vokinger
  • Tim Finkenstaedt
  • Georg Osterhoff
  • Michael A. Fischer
  • Roman Guggenberger
Computed Tomography



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.


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).


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.


Spine Machine learning Osteoporosis Tomography, X-ray computed 



Artificial neural networks


Bone mineral density


Concordance correlation coefficient


Dual-energy X-ray absorptiometry


Finite element analysis


Gray-level co-occurrence matrix


Gray-level run-length matrix


High-resolution peripheral quantitative computed tomography


Image histogram


Machine learning


Multi-layer perceptron


Random forest


Region of interest


Support vector machine


Texture analysis


Thoracic-lumbar junction



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

Compliance with ethical standards


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.


• retrospective

• case-control study

• performed at one institution

Supplementary material

330_2018_5846_MOESM1_ESM.docx (20 kb)
ESM 1 (DOCX 20 kb)


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Institute of Diagnostic and Interventional RadiologyUniversity Hospital ZurichZurichSwitzerland
  2. 2.University Hospital of ZurichZurichSwitzerland
  3. 3.University of ZurichZurichSwitzerland
  4. 4.Department of TraumaUniversity Hospital ZurichZurichSwitzerland
  5. 5.Department of RadiologyUniversity Hospital Balgrist, University of ZurichZurichSwitzerland

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