Open AccessMachine-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 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).