Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

Wang, L., Wang, H., D’Angelo, F., Curtin, L., Sereduk, C. P., Leon, G. D., Singleton, K. W., Urcuyo, J., Hawkins-Daarud, A., Jackson, P. R., Krishna, C., Zimmerman, R. S., Patra, D. P., Bendok, B. R., Smith, K. A., Nakaji, P., Donev, K., Baxter, L. C., Mrugała, M. M., … Li, J. (2024). Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm. PLOS ONE, 19(4), e0299267. https://doi.org/10.1371/journal.pone.0299267
Authors:
Lujia Wang
Hairong Wang
Fulvio D'Angelo
Lee Curtin
Christopher P Sereduk
Gustavo De Leon
Kyle W Singleton
Javier Urcuyo
Andrea Hawkins-Daarud
Pamela R Jackson
Chandan Krishna
Richard S Zimmerman
Devi P Patra
Bernard R Bendok
Kris A Smith
Peter Nakaji
Kliment Donev
Leslie C Baxter
Maciej M Mrugała
Michele Ceccarelli
Antonio Iavarone
Kristin R Swanson
Nhan L Tran
Leland S Hu
Jing Li
Affiliated Authors:
Fulvio D'Angelo
Antonio Iavarone
Columbia Affiliation:
Subjects:
Radiomics in Medical Imaging Analysis (OpenAlex Topic)
Gliomas (OpenAlex Topic)
Deep Learning in Medical Image Analysis (OpenAlex Topic)
Glioblastoma (MeSH)
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Publication Type:
Article
Unique ID:
10.1371/journal.pone.0299267
PMID:
38568950
DOI:
10.1371/journal.pone.0299267
Journal:
PLOS ONE
Publication Date:
Data Source:
PubMed
Source Link:
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