Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
The Gauss-Tree: Efficient Object Identification in Databases of Probabilistic Feature Vectors
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Improving Lesion-Symptom Mapping
Journal of Cognitive Neuroscience
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Applying spatial distribution analysis techniques to classification of 3D medical images
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
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.