Artikel dengan mandat akses publik - David A. Hormuth, IIPelajari lebih lanjut
Tidak tersedia di mana pun: 3
Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment
RT Woodall, DA Hormuth II, MRA Abdelmalik, C Wu, X Feng, WT Phillips, ...
Medical Imaging 2019: Physics of Medical Imaging 10948, 890-902, 2019
Mandat: US National Institutes of Health
Quantitative imaging to guide mechanism-based modeling of cancer
DA Hormuth, MT McKenna, TE Yankeelov
Radiomics and Radiogenomics, 369-385, 2019
Mandat: US National Institutes of Health, Cancer Prevention Research Institute of …
In vivo imaging to initialize a biophysical model of tumor growth: Preliminary results
DA Hormuth, TE Yankeelov
2013 Biomedical Sciences and Engineering Conference (BSEC), 1-4, 2013
Mandat: US National Institutes of Health
Tersedia di suatu tempat: 58
The 2019 mathematical oncology roadmap
RC Rockne, A Hawkins-Daarud, KR Swanson, JP Sluka, JA Glazier, ...
Physical biology 16 (4), 041005, 2019
Mandat: US National Institutes of Health, UK Medical Research Council
Clinically relevant modeling of tumor growth and treatment response
TE Yankeelov, N Atuegwu, D Hormuth, JA Weis, SL Barnes, MI Miga, ...
Science translational medicine 5 (187), 187ps9-187ps9, 2013
Mandat: US National Institutes of Health
Mathematical models of tumor cell proliferation: A review of the literature
AM Jarrett, EABF Lima, DA Hormuth, MT McKenna, X Feng, DA Ekrut, ...
Expert review of anticancer therapy 18 (12), 1271-1286, 2018
Mandat: US National Institutes of Health
Selection, calibration, and validation of models of tumor growth
E Lima, JT Oden, DA Hormuth, TE Yankeelov, RC Almeida
Mathematical Models and Methods in Applied Sciences 26 (12), 2341-2368, 2016
Mandat: US National Science Foundation, US Department of Energy, US National …
A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth
DA Hormuth, JA Weis, SL Barnes, MI Miga, EC Rericha, V Quaranta, ...
Journal of The Royal Society Interface 14 (128), 20161010, 2017
Mandat: US National Institutes of Health
Selection and validation of predictive models of radiation effects on tumor growth based on noninvasive imaging data
E Lima, JT Oden, B Wohlmuth, A Shahmoradi, DA Hormuth II, ...
Computer methods in applied mechanics and engineering 327, 277-305, 2017
Mandat: US National Science Foundation, US Department of Energy, US National …
Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data
DA Hormuth II, JA Weis, SL Barnes, MI Miga, EC Rericha, V Quaranta, ...
Physical biology 12 (4), 046006, 2015
Mandat: US National Institutes of Health
Optimal control theory for personalized therapeutic regimens in oncology: Background, history, challenges, and opportunities
AM Jarrett, D Faghihi, DA Hormuth, EABF Lima, J Virostko, G Biros, D Patt, ...
Journal of clinical medicine 9 (5), 1314, 2020
Mandat: US National Institutes of Health, Cancer Prevention Research Institute of …
Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors
C Wu, F Pineda, DA Hormuth, GS Karczmar, TE Yankeelov
Magnetic resonance in medicine 81 (3), 2147-2160, 2019
Mandat: US National Institutes of Health
Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory …
AM Jarrett, DA Hormuth, SL Barnes, X Feng, W Huang, TE Yankeelov
Physics in Medicine & Biology 63 (10), 105015, 2018
Mandat: US National Institutes of Health
Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
DA Hormuth, KA Al Feghali, AM Elliott, TE Yankeelov, C Chung
Scientific reports 11 (1), 8520, 2021
Mandat: US National Institutes of Health, Cancer Prevention Research Institute of …
Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology
C Wu, G Lorenzo, DA Hormuth, EABF Lima, KP Slavkova, JC DiCarlo, ...
Biophysics reviews 3 (2), 2022
Mandat: US National Institutes of Health, European Commission, Cancer Prevention …
Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data
AM Jarrett, DA Hormuth II, C Wu, AS Kazerouni, DA Ekrut, J Virostko, ...
Neoplasia 22 (12), 820-830, 2020
Mandat: US National Institutes of Health, Cancer Prevention Research Institute of …
Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer
AM Jarrett, A Shah, MJ Bloom, MT McKenna, DA Hormuth, TE Yankeelov, ...
Scientific reports 9 (1), 12830, 2019
Mandat: US National Institutes of Health, Cancer Prevention Research Institute of …
Mechanically coupled reaction-diffusion model to predict glioma growth: methodological details
DA Hormuth, SL Eldridge, JA Weis, MI Miga, TE Yankeelov
Cancer systems biology: methods and protocols, 225-241, 2018
Mandat: US National Institutes of Health
Mechanism-based modeling of tumor growth and treatment response constrained by multiparametric imaging data
DA Hormuth, AM Jarrett, EABF Lima, MT McKenna, DT Fuentes, ...
JCO clinical cancer informatics 3, 1-10, 2019
Mandat: US National Institutes of Health
Biologically-based mathematical modeling of tumor vasculature and angiogenesis via time-resolved imaging data
DA Hormuth, CM Phillips, C Wu, EABF Lima, G Lorenzo, PK Jha, ...
Cancers 13 (12), 3008, 2021
Mandat: US National Institutes of Health, European Commission, Cancer Prevention …
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