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Improved prediction of radiation pneumonitis by combining b..:

Tonaye Hinton ; David Karnak ; Ming Tang...
http://www.sciencedirect.com/science/article/pii/S1936523322000870. , 2022

Link: https://doi.org/10.1016/j.tranon.2022.101428

RT Journal
T1 <Title>Improved prediction of radiation pneumonitis by combining biological and radiobiological parameters using a data-driven Bayesian network analysis</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:14a26493ad054b57b6d1c4139b2ae385&Exemplar=1&LAN=DE
				
A1 Tonaye Hinton
A1 David Karnak
A1 Ming Tang
A1 Ralph Jiang
A1 Yi Luo
A1 Philip Boonstra
A1 Yilun Sun
A1 Derek J. Nancarrow
A1 Erin Sandford
A1 Paramita Ray
A1 Christopher Maurino
A1 Martha Matuszak
A1 Matthew J. Schipper
A1 Michael D. Green
A1 Gregory A. Yanik
A1 Muneesh Tewari
A1 Issam El Naqa
A1 Caitlin A. Schonewolf
A1 Randall Ten Haken
A1 Shruti Jolly
A1 Theodore S. Lawrence
A1 Dipankar Ray
PB Elsevier
YR 2022
K1 Radiation pneumonitis
K1 Inflammatory cytokines
K1 MicroRNA (miRNA)
K1 Tumor necrosis factor alpha (TNFα) signaling
K1 nuclear factor kappa B (NFκB)
K1 Data-driven Bayesian network (DD-BN) analysis
K1 Neoplasms. Tumors. Oncology. Including cancer and carcinogens
K1 RC254-282
JF http://www.sciencedirect.com/science/article/pii/S1936523322000870
LK http://dx.doi.org/https://doi.org/10.1016/j.tranon.2022.101428
DO https://doi.org/10.1016/j.tranon.2022.101428
SF ELIB - SuUB Bremen

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