Usability and Potential of Geostatistics for Spatial Discrimination of Multiple Sclerosis Lesion Patterns

Marschallinger, Robert; Golaszewski, Stefan; Kunz, Alexander; Kronbichler, Martin; Ladurner, G.; Hofmann, Peter; Trinka, Eugen; McCoy, Mark R.; Kraus, Jörg

doi:10.1111/jon.12000

Cited by 4 publications

(13 citation statements)

References 30 publications

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“…Moreover, empirical variograms of MS‐lesion patterns have to be limited to distances of 15 mm to enable meaningful variogram model fitting (Marschallinger et al. ). In making this restriction, some information on pattern granularity like repetitions (the so‐called hole effect) is lost.…”

Section: Discussionmentioning

confidence: 99%

“…When MS‐lesion patterns are normalized to MNI space, variography enables single patient follow‐up analysis, and intra or intergroup analysis (Marschallinger et al. ). The empirical variogram γ ( h ) is calculated using (eq.…”

Section: Methodsmentioning

confidence: 99%

“…) was found to be the most suitable for quantifying the MS‐lesion patterns (Marschallinger et al. ).

γ false(boldh false) = c \cdot (1 - \frac{e^{(- 3 \cdot | h |)}}{a})

…”

Section: Methodsmentioning

confidence: 99%

“…When lesion patterns are geometrically normalized, their geometry can be conveniently portrayed and compared in a diagram of a versus c ( ac ‐plot (Marschallinger et al. )).…”

Section: Methodsmentioning

confidence: 99%

“…These lesions present rather heterogeneously across patients not only with regard to the number and overall volume but also with regard to spatial pattern, predilection sites, and shape of single lesions (Filippi and Rocca 2011). Researching the geometrical configurations of white matter MS-lesions from MRI investigation is considered an opportunity for greater understanding of the relationship between MS clinics and neuroradiological findings (Pham et al 2010;Marschallinger et al 2014;Taschler et al 2014). Until now, the heterogeneity of MRI findings could not be related fully to the heterogeneity of the disease course.…”

Section: Introductionmentioning

confidence: 99%

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A MS‐lesion pattern discrimination plot based on geostatistics

et al. 2016

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IntroductionA geostatistical approach to characterize MS‐lesion patterns based on their geometrical properties is presented.MethodsA dataset of 259 binary MS‐lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill.ResultsParameters Range and Sill correlate with MS‐lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS‐lesion patterns based on geometry: the so‐called MS‐Lesion Pattern Discrimination Plot.ConclusionsThe geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross‐sectional, follow‐up, and medication impact analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…) was found to be the most suitable for quantifying the MS‐lesion patterns (Marschallinger et al. ).

γ false(boldh false) = c \cdot (1 - \frac{e^{(- 3 \cdot | h |)}}{a})

…”

Section: Methodsmentioning

confidence: 99%

“…When lesion patterns are geometrically normalized, their geometry can be conveniently portrayed and compared in a diagram of a versus c ( ac ‐plot (Marschallinger et al. )).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A MS‐lesion pattern discrimination plot based on geostatistics

et al. 2016

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Using Classical Geostatistics to Quantify the Spatiotemporal Dynamics of a Neurodegenerative Disease from Brain MRI

Marschallinger

Mühlau

Schmidt

et al. 2017

Geostatistics Valencia 2016

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Geostatistical Analysis of White Matter Lesions in Multiple Sclerosis Identifies Gender Differences in Lesion Evolution

Marschallinger

Mühlau

Pongratz

et al. 2018

Front. Mol. Neurosci.

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Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system with presumed autoimmune origin. The development of lesions within the gray matter and white matter, which are highly variable with respect to number, total volume, morphology and spatial evolution and which only show a limited correlation with clinical disability, is a hallmark of the disease. Population-based studies indicate a distinct outcome depending on gender. Here, we studied gender-related differences in the evolution of white matter MS-lesions (MS-WML) in early MS by using geostatistical methods. Within a 3 years observation period, a female and a male MS patient group received disease modifying drugs and underwent standardized annual brain magnetic resonance imaging, accompanied by neurological examination. MS-WML were automatically extracted and the derived binary lesion masks were subject to geostatistical analysis, yielding quantitative spatial-statistics metrics on MS-WML pattern morphology and total lesion volume (TLV). Through the MS-lesion pattern discrimination plot, the following differences were disclosed: corresponding to gender and MS-WML pattern morphology at baseline, two female subgroups (F1, F2) and two male subgroups (M1, M2) are discerned that follow a distinct MS-WML pattern evolution in space and time. F1 and M1 start with medium-level MS-WML pattern smoothness and TLV, both behave longitudinally quasi-static. By contrast, F2 and M2 start with high-level MS-WML pattern smoothness and medium-level TLV. F2 and M2 longitudinal development is characterized by strongly diminishing MS-WML pattern smoothness and TLV, i.e., continued shrinking and break-up of MS-WML. As compared to the male subgroup M2, the female subgroup F2 shows continued, increased MS-WML pattern smoothness and TLV. Data from neurological examination suggest a correlation of MS-WML pattern morphology metrics and EDSS. Our results justify detailed studies on gender-related differences.

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Usability and Potential of Geostatistics for Spatial Discrimination of Multiple Sclerosis Lesion Patterns

Abstract: This method in development is a promising candidate to complement standard image-based statistics by incorporating spatial quantification. The work flow is generic and not limited to analyzing MS lesion patterns. It can be completely automated for the screening of radiological archives.

Cited by 4 publications

References 30 publications

A MS‐lesion pattern discrimination plot based on geostatistics

A MS‐lesion pattern discrimination plot based on geostatistics

Using Classical Geostatistics to Quantify the Spatiotemporal Dynamics of a Neurodegenerative Disease from Brain MRI

Geostatistical Analysis of White Matter Lesions in Multiple Sclerosis Identifies Gender Differences in Lesion Evolution

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