Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers.
Fei DongQian LiBiao JiangXiuliang ZhuQiang ZengPeiyu HuangShujun ChenMinming ZhangPublished in: European radiology (2020)
• Radiomics provides a way to differentiate single brain MET between GBM by using conventional MR images. • The results of classifiers or algorithms themselves are also data, the transformation of the primary data. • Like MDT consultation, the combined use of multiple classifiers may confer extra benefits.
Keyphrases
- resting state
- electronic health record
- white matter
- deep learning
- big data
- machine learning
- functional connectivity
- palliative care
- contrast enhanced
- cerebral ischemia
- magnetic resonance
- convolutional neural network
- tyrosine kinase
- magnetic resonance imaging
- squamous cell carcinoma
- data analysis
- brain injury
- optical coherence tomography
- artificial intelligence
- lymph node metastasis
- computed tomography
- multiple sclerosis
- subarachnoid hemorrhage
- blood brain barrier