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Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI
Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric in...
Autores principales: | Gaw, Nathan, Hawkins-Daarud, Andrea, Hu, Leland S., Yoon, Hyunsoo, Wang, Lujia, Xu, Yanzhe, Jackson, Pamela R., Singleton, Kyle W., Baxter, Leslie C., Eschbacher, Jennifer, Gonzales, Ashlyn, Nespodzany, Ashley, Smith, Kris, Nakaji, Peter, Mitchell, J. Ross, Wu, Teresa, Swanson, Kristin R., Li, Jing |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624304/ https://www.ncbi.nlm.nih.gov/pubmed/31296889 http://dx.doi.org/10.1038/s41598-019-46296-4 |
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