| The 2019 mathematical oncology roadmap RC Rockne, A Hawkins-Daarud, KR Swanson, JP Sluka, JA Glazier, ... Physical biology 16 (4), 041005, 2019 | 191 | 2019 |
| 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 | 136 | 2018 |
| Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect KE Johnson, G Howard, W Mo, MK Strasser, EABF Lima, S Huang, ... PLoS biology 17 (8), e3000399, 2019 | 105 | 2019 |
| Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil JF Oliveira, DCP Jorge, RV Veiga, MS Rodrigues, MF Torquato, ... Nature communications 12 (1), 333, 2021 | 104 | 2021 |
| 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 | 102 | 2016 |
| Toward predictive multiscale modeling of vascular tumor growth: computational and experimental oncology for tumor prediction JT Oden, EABF Lima, RC Almeida, Y Feng, MN Rylander, D Fuentes, ... Archives of Computational Methods in Engineering 23, 735-779, 2016 | 101 | 2016 |
| 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 | 90 | 2017 |
| A hybrid ten-species phase-field model of tumor growth E Lima, JT Oden, RC Almeida Mathematical Models and Methods in Applied Sciences 24 (13), 2569-2599, 2014 | 79 | 2014 |
| 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 |
| A hybrid model of tumor growth and angiogenesis: In silico experiments CM Phillips, EABF Lima, RT Woodall, A Brock, TE Yankeelov Plos one 15 (4), e0231137, 2020 | 59 | 2020 |
| 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 |
| 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 |
| A hybrid three-scale model of tumor growth HL Rocha, RC Almeida, E Lima, ACM Resende, JT Oden, TE Yankeelov Mathematical Models and Methods in Applied Sciences 28 (01), 61-93, 2018 | 40 | 2018 |
| Analysis and numerical solution of stochastic phase‐field models of tumor growth EABF Lima, RC Almeida, JT Oden Numerical methods for partial differential equations 31 (2), 552-574, 2015 | 40 | 2015 |
| Ecological modeling and pest population management: a possible and necessary connection in a changing world EABF Lima, CP Ferreira, WAC Godoy Neotropical Entomology 38, 699-707, 2009 | 38 | 2009 |
| Local and nonlocal phase-field models of tumor growth and invasion due to ECM degradation M Fritz, EABF Lima, V Nikolić, JT Oden, B Wohlmuth Mathematical Models and Methods in Applied Sciences 29 (13), 2433-2468, 2019 | 33 | 2019 |
| On the unsteady Darcy–Forchheimer–Brinkman equation in local and nonlocal tumor growth models M Fritz, EABF Lima, J Tinsley Oden, B Wohlmuth Mathematical Models and Methods in Applied Sciences 29 (09), 1691-1731, 2019 | 33 | 2019 |
| Quantitative in vivo imaging to enable tumour forecasting and treatment optimization G Lorenzo, DA Hormuth II, AM Jarrett, EABF Lima, S Subramanian, ... Cancer, complexity, computation, 55-97, 2022 | 32 | 2022 |
| Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth EABF Lima, D Faghihi, R Philley, J Yang, J Virostko, CM Phillips, ... PLoS Computational Biology 17 (11), e1008845, 2021 | 31 | 2021 |
Thomas YankeelovDirector, Center for Computational Oncology, The University of Texas at AustinVerified email at utexas.edu
