Developing a Radiomics Signature for Supratentorial Extra-Ventricular Ependymoma Using Multimodal MR Imaging

Rationale and Objectives: To build a machine learning-based diagnostic model that can accurately distinguish adult supratentorial extraventricular ependymoma (STEE) from similarly appearing high-grade gliomas (HGG) using quantitative radiomic signatures from a multi-parametric MRI framework. Materia...

Descripción completa

Detalles Bibliográficos
Autores principales: Safai, Apoorva, Shinde, Sumeet, Jadhav, Manali, Chougule, Tanay, Indoria, Abhilasha, Kumar, Manoj, Santosh, Vani, Jabeen, Shumyla, Beniwal, Manish, Konar, Subhash, Saini, Jitender, Ingalhalikar, Madhura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Neurology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339322/
https://www.ncbi.nlm.nih.gov/pubmed/34367044
http://dx.doi.org/10.3389/fneur.2021.648092

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339322/
https://www.ncbi.nlm.nih.gov/pubmed/34367044
http://dx.doi.org/10.3389/fneur.2021.648092

Ejemplares similares