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Prediction of malignancy grading using computed tomography perfusion imaging in nonenhancing supratentorial gliomas

✍ Scribed by Takaaki Beppu; Makoto Sasaki; Kohsuke Kudo; Akira Kurose; Masaru Takeda; Hiroshi Kashimura; Akira Ogawa; Kuniaki Ogasawara


Publisher
Springer US
Year
2010
Tongue
English
Weight
413 KB
Volume
103
Category
Article
ISSN
0167-594X

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## Abstract The advantages of predictive modeling in glioma grading from MR perfusion images have not yet been explored. The aim of the current study was to implement a predictive model based on support vector machines (SVM) for glioma grading using tumor blood volume histogram signatures derived f