Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.

Sha-Sha ZhaoXiu-Long FengYu-Chuan HuYu HanQiang TianYing-Zhi SunJie ZhangXiang-Wei GeSi-Chao ChengXiu-Li LiLi MaoShu-Ning ShenLin-Feng YanGuang-Bin CuiWen Wang

Published in: BMC neurology (2020)

Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Keyphrases

contrast enhanced
machine learning
diffusion weighted
magnetic resonance imaging
artificial intelligence
magnetic resonance
computed tomography
deep learning
diffusion weighted imaging
big data
squamous cell carcinoma
dual energy
energy transfer