Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images | Zendy

Jiwoong Jeong | Zendy; Liya Wang | Zendy; Bing Ji | Zendy; Yang Lei | Zendy; Arif N. Ali | Zendy; Tian Liu | Zendy; Walter J. Curran | Zendy; Hui Mao | Zendy; Xiaofeng Yang | Zendy

Open Access

Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images

Author(s) -

Jiwoong Jeong,

Liya Wang,

Bing Ji,

Yang Lei,

Arif N. Ali,

Tian Liu,

Walter J. Curran,

Hui Mao,

Xiaofeng Yang

Publication year - 2019

Publication title -

quantitative imaging in medicine and surgery

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.766

H-Index - 21

eISSN - 2223-4292

pISSN - 2223-4306

DOI - 10.21037/qims.2019.07.01

Subject(s) - fluid attenuated inversion recovery , random forest , glioblastoma , grading (engineering) , magnetic resonance imaging , receiver operating characteristic , brain tumor , artificial intelligence , feature selection , glioma , feature (linguistics) , contrast (vision) , computer science , pattern recognition (psychology) , nuclear medicine , medicine , radiology , pathology , machine learning , cancer research , linguistics , philosophy , civil engineering , engineering

Glioblastoma is the most aggressive brain tumor with poor prognosis. The purpose of this study is to improve the tissue characterization of these highly heterogeneous tumors using delta-radiomic features of images from dynamic susceptibility contrast enhanced (DSC) magnetic resonance imaging (MRI).

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