Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions



To evaluate the diagnostic performance of texture analysis (TA) applied on non-contrast-enhanced susceptibility-weighted imaging (SWI) to differentiate acute (enhancing) from chronic (non-enhancing) multiple sclerosis (MS) lesions.


We analyzed 175 lesions from 58 patients with relapsing-remitting MS imaged on a 3.0 T MRI scanner and applied TA on T2-w and SWI images to extract texture features. We evaluated the presence or absence of lesion enhancement on T1-w post-contrast images and performed a computational statistical analysis to assess if there was any significant correlation between the texture features and the presence of lesion activity. ROC curves and leave-one-out cross-validation were used to evaluate the performance of individual features and multiparametric models in the identification of active lesions.


Multiple TA features obtained from SWI images showed a significantly different distribution in acute and chronic lesions (AUC, 0.617–0.720). Multiparametric predictive models based on logistic ridge regression and partial least squares regression yielded an AUC of 0.778 and 0.808, respectively. Results from T2-w images did not show any significant predictive ability of neither individual features nor multiparametric models.


Texture analysis on SWI sequences may be useful to differentiate acute from chronic MS lesions. The good diagnostic performance could help to reduce the need of intravenous contrast agent administration in follow-up MRI studies.

Key Points

Texture analysis applied on SWI sequences may be useful to differentiate acute from chronic multiple sclerosis lesions

The good diagnostic performance could help to minimize the need of intravenous contrast agent administration in follow-up MRI studies

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Area under the ROC curve


Gray-level co-occurrence matrix


Gray-level run length matrix


Gray-level zone length matrix


Magnetic resonance imaging


Multiple sclerosis


Neighborhood gray-level different matrix


Region of interest


Standard deviation


Susceptibility-weighted imaging


Texture analysis


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

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Corresponding author

Correspondence to Giovanni Caruana.

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The scientific guarantor of this publication is Dr. Àlex Rovira (Neuroradiology Section, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Pg. Vall d’Hebron 119-129, 08035 Barcelona, Spain).

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

Dr. Àlex Rovira serves on scientific advisory boards for Novartis, Sanofi-Genzyme, SyntheticMR, Bayer, Roche, Biogen, Neurodiem and OLEA Medical, and has received speaker honoraria from Bayer, Sanofi-Genzyme, Bracco, Merck-Serono, Teva Pharmaceutical Industries Ltd, Novartis, Roche, and Biogen.

The remaining 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

Two of the authors (Dr. Giovanni Caruana and Dr. Roberto Cannella) have significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was not required because this was a retrospective study and all the patients who underwent MRI provided written informed consent for the use of their anonymized MR studies for research purposes.


• retrospective

• diagnostic study

• performed at one institution

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Scatter plots that show the distribution of each texture feature obtained from SWI data in acute and chronic MS lesions. (DOCX 350 kb)

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Caruana, G., Pessini, L.M., Cannella, R. et al. Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions. Eur Radiol (2020). https://doi.org/10.1007/s00330-020-06995-3

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  • Multiple sclerosis
  • Magnetic resonance imaging
  • Contrast agent
  • Logistic models