Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma

Article ID	Journal	Published Year	Pages	File Type
11025277	European Journal of Radiology	2018	49 Pages	PDF

Abstract

The performance of machine learning based on histogram and texture features in multi-parametric MRI was superior to that of conventional cut-off method and the board certified radiologists to differentiate a GBM from a PCNSL.

Keywords

GLCM XGBoost T2-weighted images PCNSL ITA rCBV DWI GBM ROC ADC DSC AUC MRI Image texture analysis diffusion-weighted imaging Magnetic resonance imaging Extreme gradient boosting Relative cerebral blood volume CNS two dimensional central nervous system apparent diffusion coefficient Lymphoma area under the curve Gadolinium Glioblastoma multiforme receiver operating characteristic Machine learning

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Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma

Authors

Masataka Nakagawa, Takeshi Nakaura, Tomohiro Namimoto, Mika Kitajima, Hiroyuki Uetani, Machiko Tateishi, Seitaro Oda, Daisuke Utsunomiya, Keishi Makino, Hideo Nakamura, Akitake Mukasa, Toshinori Hirai, Yasuyuki Yamashita,