NDLI: A novel tumor grading technique using functional magnetic resonance imaging

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A novel tumor grading technique using functional magnetic resonance imaging

Content Provider	ACM Digital Library
Author	Haegler, Katrin Böhm, Christian
Abstract	Adequate therapy planning of gliomas needs histological determination of invasive biopsy due to the fact that both treatment and prognosis of glial neoplasms vary strongly depending on their histological grading. Magnetic resonance imaging based glioma grading is currently mainly based on contrast-enhanced T1-weighted images. To additionally gain information on tumor physiology for glioma grading functional MRI techniques like perfusion MR have also been considered. Here, we present a novel technique for glioma grading, which is based on a similarity search for uncertain data. In order to perform the search as accurate as possible we used four different features of the tumors as input for the similarity search: the Cerebral Blood Volume, the Cerebral Blood Flow, the Mean Transit Time, and the post-contrast T1-weighted image. For each patient a tumor feature vector was defined by a four-dimensional Probability Density Function, more precisely a Gaussian Mixture Model (GMM). In contrast to existing similarity searches we also considered correlations between different features in the similarity search. Here, we present the entire workflow from image acquisition to tumor grade prediction providing a probability-based classifier for gliomas. We applied our approach to MRI data sets of glioma patients, which were preprocessed and converted to four-dimensional GMMs, and achieved an accuracy, sensitivity, and specificity of 83.8%, 78.6%, and 87.0% while grading based solely on contrast-enhancement could only achieve an accuracy, sensitivity, and specificity of 64.9%, 52.4%, and 81.3%, respectively. Hence, our proposed similarity search based grading technique seems to be of great value for supporting non-invasive tumor grading since it integrates the information of different MRI sequences including perfusion maps in one semi-automatic analysis.
Starting Page	18
Ending Page	26
Page Count	9
File Format	PDF
ISBN	9781450308434
DOI	10.1145/2023582.2023586
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Perfusion mri Glioma grading Uncertain data Gaussian mixture model Similarity search
Content Type	Text
Resource Type	Article

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