Three-dimensional susceptibility-weighted imaging at 7 T using fractal-based quantitative analysis to grade gliomas View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-08-18

AUTHORS

Antonio Di Ieva, Sabine Göd, Günther Grabner, Fabio Grizzi, Camillo Sherif, Christian Matula, Manfred Tschabitscher, Siegfrid Trattnig

ABSTRACT

IntroductionSusceptibility-weighted imaging (SWI) with high- and ultra-high-field magnetic resonance is a very helpful tool for evaluating brain gliomas and intratumoral structures, including microvasculature. Here, we test whether objective quantification of intratumoral SWI patterns by applying fractal analysis can offer reliable indexes capable of differentiating glial tumor grades.MethodsThirty-six patients affected by brain gliomas (grades II–IV, according to the WHO classification system) underwent MRI at 7 T using a SWI protocol. All images were collected and analyzed by applying a computer-aided fractal image analysis, which applies the fractal dimension as a measure of geometrical complexity of intratumoral SWI patterns. The results were subsequently statistically correlated to the histopathological tumor grade.ResultsThe mean value of the fractal dimension of the intratumoral SWI patterns was 2.086 ± 0.413. We found a trend of higher fractal dimension values in groups of higher histologic grade. The values ranged from a mean value of 1.682 ± 0.278 for grade II gliomas to 2.247 ± 0.358 for grade IV gliomas (p = 0.013); there was an overall statistically significant difference between histopathological groups.ConclusionThe present study confirms that SWI at 7 T is a useful method for detecting intratumoral vascular architecture of brain gliomas and that SWI pattern quantification by means of fractal dimension offers a potential objective morphometric image biomarker of tumor grade. More... »

PAGES

35-40

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00234-012-1081-1

DOI

http://dx.doi.org/10.1007/s00234-012-1081-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002698516

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22903580


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