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Published in: European Radiology 11/2020

01-11-2020 | Magnetic Resonance Imaging | Neuro

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

Authors: Giovanni Caruana, Lucas M. Pessini, Roberto Cannella, Giuseppe Salvaggio, Andréa de Barros, Annalaura Salerno, Cristina Auger, Àlex Rovira

Published in: European Radiology | Issue 11/2020

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Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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
Appendix
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Literature
1.
go back to reference Confavreux C, Vukusic S, Moreau T, Adeleine P (2000) Relapses and progression of disability in multiple sclerosis. N Engl J Med 343:1430–1438CrossRef Confavreux C, Vukusic S, Moreau T, Adeleine P (2000) Relapses and progression of disability in multiple sclerosis. N Engl J Med 343:1430–1438CrossRef
2.
go back to reference Rovira À, León A (2008) MR in the diagnosis and monitoring of multiple sclerosis: an overview. Eur J Radiol 67:409–414CrossRef Rovira À, León A (2008) MR in the diagnosis and monitoring of multiple sclerosis: an overview. Eur J Radiol 67:409–414CrossRef
3.
go back to reference Thompson AJ, Banwell BL, Barkhof F et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17:162–173CrossRef Thompson AJ, Banwell BL, Barkhof F et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17:162–173CrossRef
4.
go back to reference Choi JW, Moon W-J (2019) Gadolinium deposition in the brain: current updates. Korean J Radiol 20:134CrossRef Choi JW, Moon W-J (2019) Gadolinium deposition in the brain: current updates. Korean J Radiol 20:134CrossRef
5.
go back to reference Zhang B, Liang L, Chen W, Liang C, Zhang S (2015) An updated study to determine association between gadolinium-based contrast agents and nephrogenic systemic fibrosis. PLoS One 10:e0129720CrossRef Zhang B, Liang L, Chen W, Liang C, Zhang S (2015) An updated study to determine association between gadolinium-based contrast agents and nephrogenic systemic fibrosis. PLoS One 10:e0129720CrossRef
6.
go back to reference Kutzelnigg A, Lassmann H (2014) Pathology of multiple sclerosis and related inflammatory demyelinating diseases. Handb Clin Neurol 122:15–58 Kutzelnigg A, Lassmann H (2014) Pathology of multiple sclerosis and related inflammatory demyelinating diseases. Handb Clin Neurol 122:15–58
7.
go back to reference Mehta V, Pei W, Yang G et al (2013) Iron is a sensitive biomarker for inflammation in multiple sclerosis lesions. PLoS One 8:e57573CrossRef Mehta V, Pei W, Yang G et al (2013) Iron is a sensitive biomarker for inflammation in multiple sclerosis lesions. PLoS One 8:e57573CrossRef
8.
go back to reference Haacke EM, Makki M, Ge Y et al (2009) Characterizing iron deposition in multiple sclerosis lesions using susceptibility weighted imaging. J Magn Reson Imaging 29:537–544CrossRef Haacke EM, Makki M, Ge Y et al (2009) Characterizing iron deposition in multiple sclerosis lesions using susceptibility weighted imaging. J Magn Reson Imaging 29:537–544CrossRef
9.
go back to reference Zhang Y, Gauthier SA, Gupta A et al (2016) Longitudinal change in magnetic susceptibility of new enhanced multiple sclerosis (MS) lesions measured on serial quantitative susceptibility mapping (QSM). J Magn Reson Imaging 44:426–432CrossRef Zhang Y, Gauthier SA, Gupta A et al (2016) Longitudinal change in magnetic susceptibility of new enhanced multiple sclerosis (MS) lesions measured on serial quantitative susceptibility mapping (QSM). J Magn Reson Imaging 44:426–432CrossRef
10.
go back to reference Chen W, Gauthier SA, Gupta A et al (2014) Quantitative susceptibility mapping of multiple sclerosis lesions at various ages. Radiology 271:183–192CrossRef Chen W, Gauthier SA, Gupta A et al (2014) Quantitative susceptibility mapping of multiple sclerosis lesions at various ages. Radiology 271:183–192CrossRef
11.
go back to reference Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503CrossRef Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503CrossRef
12.
go back to reference Cannella R, Rangaswamy B, Minervini MI, Borhani AA, Tsung A, Furlan A (2019) Value of texture analysis on gadoxetic acid–enhanced MRI for differentiating hepatocellular adenoma from focal nodular hyperplasia. AJR Am J Roentgenol 212:538–546CrossRef Cannella R, Rangaswamy B, Minervini MI, Borhani AA, Tsung A, Furlan A (2019) Value of texture analysis on gadoxetic acid–enhanced MRI for differentiating hepatocellular adenoma from focal nodular hyperplasia. AJR Am J Roentgenol 212:538–546CrossRef
13.
go back to reference Yu O, Mauss Y, Zollner G, Namer I, Chambron J (1999) Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results. Magn Reson Imaging 17:1261–1267CrossRef Yu O, Mauss Y, Zollner G, Namer I, Chambron J (1999) Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results. Magn Reson Imaging 17:1261–1267CrossRef
14.
go back to reference Michoux N, Guillet A, Rommel D, Mazzamuto G, Sindic C, Duprez T (2015) Texture analysis of T2-weighted MR images to assess acute inflammation in brain MS lesions. PLoS One 10:e0145497CrossRef Michoux N, Guillet A, Rommel D, Mazzamuto G, Sindic C, Duprez T (2015) Texture analysis of T2-weighted MR images to assess acute inflammation in brain MS lesions. PLoS One 10:e0145497CrossRef
15.
go back to reference Polman CH, Reingold SC, Banwell B et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69:292–302CrossRef Polman CH, Reingold SC, Banwell B et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69:292–302CrossRef
16.
go back to reference Nioche C, Orlhac F, Boughdad S et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789CrossRef Nioche C, Orlhac F, Boughdad S et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789CrossRef
18.
go back to reference Thissen D, Steinberg L, Kuang D (2002) Quick and easy implementation of the Benjamini-Hochberg procedure for controlling the false positive rate in multiple comparisons. J Educ Behav Stat 27:77–83CrossRef Thissen D, Steinberg L, Kuang D (2002) Quick and easy implementation of the Benjamini-Hochberg procedure for controlling the false positive rate in multiple comparisons. J Educ Behav Stat 27:77–83CrossRef
19.
go back to reference Wong T-T (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit 48:2839–2846CrossRef Wong T-T (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit 48:2839–2846CrossRef
20.
go back to reference Gaitán MI, Shea CD, Evangelou IE et al (2011) Evolution of the blood-brain barrier in newly forming multiple sclerosis lesions. Ann Neurol 70:22–29CrossRef Gaitán MI, Shea CD, Evangelou IE et al (2011) Evolution of the blood-brain barrier in newly forming multiple sclerosis lesions. Ann Neurol 70:22–29CrossRef
21.
go back to reference Bagnato F, Hametner S, Yao B et al (2011) Tracking iron in multiple sclerosis: a combined imaging and histopathological study at 7 Tesla. Brain 134:3602–3615CrossRef Bagnato F, Hametner S, Yao B et al (2011) Tracking iron in multiple sclerosis: a combined imaging and histopathological study at 7 Tesla. Brain 134:3602–3615CrossRef
22.
go back to reference Wisnieff C, Ramanan S, Olesik J, Gauthier S, Wang Y, Pitt D (2015) Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron. Magn Reson Med 74:564–570CrossRef Wisnieff C, Ramanan S, Olesik J, Gauthier S, Wang Y, Pitt D (2015) Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron. Magn Reson Med 74:564–570CrossRef
23.
go back to reference Salem M, Cabezas M, Valverde S et al (2018) A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis. NeuroImage Clin 17:607–615CrossRef Salem M, Cabezas M, Valverde S et al (2018) A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis. NeuroImage Clin 17:607–615CrossRef
25.
go back to reference Mahmoud-Ghoneim D, Toussaint G, Constans J-M, de Certaines JD (2003) Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 21:983–987CrossRef Mahmoud-Ghoneim D, Toussaint G, Constans J-M, de Certaines JD (2003) Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 21:983–987CrossRef
26.
go back to reference Varghese BA, Cen SY, Hwang DH, Duddalwar VA (2019) Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol 212:520–528CrossRef Varghese BA, Cen SY, Hwang DH, Duddalwar VA (2019) Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol 212:520–528CrossRef
Metadata
Title
Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions
Authors
Giovanni Caruana
Lucas M. Pessini
Roberto Cannella
Giuseppe Salvaggio
Andréa de Barros
Annalaura Salerno
Cristina Auger
Àlex Rovira
Publication date
01-11-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 11/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06995-3

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