| New similarity search based glioma grading. | |
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MedLine Citation:
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PMID: 22160184 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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INTRODUCTION: MR-based differentiation between low- and high-grade gliomas is predominately based on contrast-enhanced T1-weighted images (CE-T1w). However, functional MR sequences as perfusion- and diffusion-weighted sequences can provide additional information on tumor grade. Here, we tested the potential of a recently developed similarity search based method that integrates information of CE-T1w and perfusion maps for non-invasive MR-based glioma grading. METHODS: We prospectively included 37 untreated glioma patients (23 grade I/II, 14 grade III gliomas), in whom 3T MRI with FLAIR, pre- and post-contrast T1-weighted, and perfusion sequences was performed. Cerebral blood volume, cerebral blood flow, and mean transit time maps as well as CE-T1w images were used as input for the similarity search. Data sets were preprocessed and converted to four-dimensional Gaussian Mixture Models that considered correlations between the different MR sequences. For each patient, a so-called tumor feature vector (= probability-based classifier) was defined and used for grading. Biopsy was used as gold standard, and similarity based grading was compared to grading solely based on CE-T1w. RESULTS: Accuracy, sensitivity, and specificity of pure CE-T1w based glioma grading were 64.9%, 78.6%, and 56.5%, respectively. Similarity search based tumor grading allowed differentiation between low-grade (I or II) and high-grade (III) gliomas with an accuracy, sensitivity, and specificity of 83.8%, 78.6%, and 87.0%. CONCLUSION: Our findings indicate that integration of perfusion parameters and CE-T1w information in a semi-automatic similarity search based analysis improves the potential of MR-based glioma grading compared to CE-T1w data alone. |
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Authors:
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Katrin Haegler; Martin Wiesmann; Christian Böhm; Jessica Freiherr; Oliver Schnell; Hartmut Brückmann; Jörg-Christian Tonn; Jennifer Linn |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-12-14 |
Journal Detail:
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Title: Neuroradiology Volume: - ISSN: 1432-1920 ISO Abbreviation: - Publication Date: 2011 Dec |
Date Detail:
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Created Date: 2011-12-13 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 1302751 Medline TA: Neuroradiology Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Department of Neuroradiology, Ludwig-Maximilians-University of Munich, Marchioninistrasse 15, 81377, Munich, Germany, katrin.haegler@med.uni-muenchen.de. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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