| The 2019 mathematical oncology roadmap RC Rockne, A Hawkins-Daarud, KR Swanson, JP Sluka, JA Glazier, ... Physical biology 16 (4), 041005, 2019 | 191 | 2019 |
| 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 | 191 | 2013 |
| 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 | 135 | 2018 |
| 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 | 101 | 2016 |
| 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 | 91 | 2017 |
| 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 | 89 | 2017 |
| 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 | 69* | 2015 |
| 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 | 68 | 2020 |
| 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 | 67 | 2019 |
| 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 | 59 | 2018 |
| 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 | 55 | 2021 |
| 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 | 54 | 2022 |
| 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 | 48 | 2020 |
| 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 | 48 | 2019 |
| 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 | 47 | 2018 |
| 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 | 46 | 2019 |
| 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 | 44 | 2021 |
| Calibrating a predictive model of tumor growth and angiogenesis with quantitative MRI DA Hormuth, AM Jarrett, X Feng, TE Yankeelov Annals of biomedical engineering 47, 1539-1551, 2019 | 39 | 2019 |
| Translating preclinical MRI methods to clinical oncology DA Hormuth, AG Sorace, J Virostko, RG Abramson, ZM Bhujwalla, ... Journal of Magnetic Resonance Imaging 50 (5), 1377-1392, 2019 | 37 | 2019 |
| Biophysical modeling of in vivo glioma response after whole-brain radiation therapy in a murine model of brain cancer DA Hormuth II, JA Weis, SL Barnes, MI Miga, V Quaranta, TE Yankeelov International Journal of Radiation Oncology* Biology* Physics 100 (5), 1270-1279, 2018 | 36 | 2018 |
Thomas YankeelovDirector, Center for Computational Oncology, The University of Texas at AustinEmail yang diverifikasi di utexas.edu
