Radiomic Signatures of Posterior Fossa Ependymoma: Molecular Subgroups and Risk Profiles.
Michael ZhangEdward WangDerek W YeciesLydia T TamMichelle HanSebastian M ToescuJason N WrightEmre AltinmakasEric ChenAlireza RadmaneshJordan NemelkaOzgur OztekinMatthias W WagnerRobert M LoberBirgit B Ertl-WagnerChang Y HoKshitij MankadNicholas A VitanzaSamuel H CheshierTom S JacquesPaul G FisherKristian AquilinaMourad SaidAlok JajuStefan PfisterMichael D TaylorGerald A GrantSarah MattonenVijay RamaswamyKristen W YeomPublished in: Neuro-oncology (2021)
We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.
Keyphrases
- risk assessment
- machine learning
- contrast enhanced
- magnetic resonance imaging
- clinical trial
- study protocol
- high resolution
- randomized controlled trial
- human health
- stem cells
- genome wide
- artificial intelligence
- magnetic resonance
- dna methylation
- heavy metals
- lymph node metastasis
- big data
- phase ii
- cell therapy
- diffusion weighted imaging
- climate change