Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers

INTRODUCTION: Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic...

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Detalles Bibliográficos
Autores principales: Haldar, Debanjan, Kazerooni, Anahita Fathi, Arif, Sherjeel, Familiar, Ariana, Madhogarhia, Rachel, Khalili, Nastaran, Bagheri, Sina, Anderson, Hannah, Shaikh, Ibraheem Salman, Mahtabfar, Aria, Kim, Meen Chul, Tu, Wenxin, Ware, Jefferey, Vossough, Arastoo, Davatzikos, Christos, Storm, Phillip B., Resnick, Adam, Nabavizadeh, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Neoplasia Press 2022
Materias:
Pediatric Brain Tumor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803939/
https://www.ncbi.nlm.nih.gov/pubmed/36566592
http://dx.doi.org/10.1016/j.neo.2022.100869

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803939/
https://www.ncbi.nlm.nih.gov/pubmed/36566592
http://dx.doi.org/10.1016/j.neo.2022.100869

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