Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

SIMPLE SUMMARY: Radiogenomics enables prediction of the status and prognosis of patients using non-invasively obtained imaging data. Current machine learning (ML) methods used in radiogenomics require huge datasets, which involve the handling of large heterogeneous datasets from multiple cohorts/hos...

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Detalles Bibliográficos
Autores principales: Kawaguchi, Risa K., Takahashi, Masamichi, Miyake, Mototaka, Kinoshita, Manabu, Takahashi, Satoshi, Ichimura, Koichi, Hamamoto, Ryuji, Narita, Yoshitaka, Sese, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306149/
https://www.ncbi.nlm.nih.gov/pubmed/34298824
http://dx.doi.org/10.3390/cancers13143611

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306149/
https://www.ncbi.nlm.nih.gov/pubmed/34298824
http://dx.doi.org/10.3390/cancers13143611

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