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For: Baldock AL, Rockne RC, Boone AD, Neal ML, Hawkins-Daarud A, Corwin DM, Bridge CA, Guyman LA, Trister AD, Mrugala MM, Rockhill JK, Swanson KR. From patient-specific mathematical neuro-oncology to precision medicine. Front Oncol 2013;3:62. [PMID: 23565501 DOI: 10.3389/fonc.2013.00062] [Cited by in Crossref: 48] [Cited by in F6Publishing: 44] [Article Influence: 5.3] [Reference Citation Analysis]
Number Citing Articles
1 Hormuth DA 2nd, Al Feghali KA, Elliott AM, Yankeelov TE, Chung C. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation. Sci Rep 2021;11:8520. [PMID: 33875739 DOI: 10.1038/s41598-021-87887-4] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 Roniotis A, Oraiopoulou ME, Tzamali E, Kontopodis E, Van Cauter S, Sakkalis V, Marias K. A Proposed Paradigm Shift in Initializing Cancer Predictive Models with DCE-MRI Based PK Parameters: A Feasibility Study. Cancer Inform 2015;14:7-18. [PMID: 26085787 DOI: 10.4137/CIN.S19339] [Cited by in Crossref: 2] [Article Influence: 0.3] [Reference Citation Analysis]
3 Wijeratne PA, Hipwell JH, Hawkes DJ, Stylianopoulos T, Vavourakis V. Multiscale biphasic modelling of peritumoural collagen microstructure: The effect of tumour growth on permeability and fluid flow. PLoS One 2017;12:e0184511. [PMID: 28902902 DOI: 10.1371/journal.pone.0184511] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.4] [Reference Citation Analysis]
4 Stéphanou A, Ballet P, Powathil G, Volpert V. Hybrid data-based modelling in oncology: successes, challenges and hopes. Math Model Nat Phenom 2020;15:21. [DOI: 10.1051/mmnp/2019026] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
5 Hawkins-Daarud A, Johnston SK, Swanson KR. Quantifying Uncertainty and Robustness in a Biomathematical Model-Based Patient-Specific Response Metric for Glioblastoma. JCO Clin Cancer Inform 2019;3:1-8. [PMID: 30758984 DOI: 10.1200/CCI.18.00066] [Cited by in Crossref: 13] [Cited by in F6Publishing: 7] [Article Influence: 6.5] [Reference Citation Analysis]
6 Fernández-romero A, Guillén-gonzález F, Suárez A. A Glioblastoma PDE-ODE model including chemotaxis and vasculature. ESAIM: M2AN 2022;56:407-31. [DOI: 10.1051/m2an/2022012] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Yan F, Gunay G, Valerio TI, Wang C, Wilson JA, Haddad MS, Watson M, Connell MO, Davidson N, Fung KM, Acar H, Tang Q. Characterization and quantification of necrotic tissues and morphology in multicellular ovarian cancer tumor spheroids using optical coherence tomography. Biomed Opt Express 2021;12:3352-71. [PMID: 34221665 DOI: 10.1364/BOE.425512] [Reference Citation Analysis]
8 Menshykau D, Tanaka S. Mechanistic Image-Based Modelling: Concepts and Applications. Handb Exp Pharmacol 2019;260:231-61. [PMID: 31823072 DOI: 10.1007/164_2019_328] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
9 Jarrett AM, Hormuth DA, Adhikarla V, Sahoo P, Abler D, Tumyan L, Schmolze D, Mortimer J, Rockne RC, Yankeelov TE. Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer. Sci Rep 2020;10:20518. [PMID: 33239688 DOI: 10.1038/s41598-020-77397-0] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
10 Sehl ME, Wicha MS. Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response. Methods Mol Biol 2018;1711:333-49. [PMID: 29344897 DOI: 10.1007/978-1-4939-7493-1_16] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
11 Kassab GS, An G, Sander EA, Miga MI, Guccione JM, Ji S, Vodovotz Y. Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective. Ann Biomed Eng 2016;44:2611-25. [PMID: 27015816 DOI: 10.1007/s10439-016-1596-4] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 1.7] [Reference Citation Analysis]
12 Gomez H. Quantitative analysis of the proliferative-to-invasive transition of hypoxic glioma cells. Integr Biol 2017;9:257-62. [DOI: 10.1039/c6ib00208k] [Cited by in Crossref: 6] [Article Influence: 1.2] [Reference Citation Analysis]
13 Hormuth DA 2nd, Eldridge SL, Weis JA, Miga MI, Yankeelov TE. Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details. Methods Mol Biol 2018;1711:225-41. [PMID: 29344892 DOI: 10.1007/978-1-4939-7493-1_11] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 3.3] [Reference Citation Analysis]
14 Priya R, Gomez GA, Budnar S, Acharya BR, Czirok A, Yap AS, Neufeld Z. Bistable front dynamics in a contractile medium: Travelling wave fronts and cortical advection define stable zones of RhoA signaling at epithelial adherens junctions. PLoS Comput Biol 2017;13:e1005411. [PMID: 28273072 DOI: 10.1371/journal.pcbi.1005411] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]
15 Chatterjee K, Atay N, Abler D, Bhargava S, Sahoo P, Rockne RC, Munson JM. Utilizing Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Analyze Interstitial Fluid Flow and Transport in Glioblastoma and the Surrounding Parenchyma in Human Patients. Pharmaceutics 2021;13:212. [PMID: 33557069 DOI: 10.3390/pharmaceutics13020212] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Comas S, Luguera E, Molero J, Balaña C, Estival A, Castañer S, Carrato C, Hostalot C, Teixidor P, Villà S. Influence of glioblastoma contact with the subventricular zone on survival and recurrence patterns. Clin Transl Oncol 2021;23:554-64. [PMID: 32728970 DOI: 10.1007/s12094-020-02448-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
17 Henscheid N. Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data. Math Biosci Eng 2020;17:6531-56. [PMID: 33378865 DOI: 10.3934/mbe.2020341] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Rockne RC, Frankel P. Mathematical Modeling in Radiation Oncology. In: Wong JY, Schultheiss TE, Radany EH, editors. Advances in Radiation Oncology. Cham: Springer International Publishing; 2017. pp. 255-71. [DOI: 10.1007/978-3-319-53235-6_12] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
19 Christ B, Dahmen U, Herrmann KH, König M, Reichenbach JR, Ricken T, Schleicher J, Ole Schwen L, Vlaic S, Waschinsky N. Computational Modeling in Liver Surgery. Front Physiol 2017;8:906. [PMID: 29249974 DOI: 10.3389/fphys.2017.00906] [Cited by in Crossref: 13] [Cited by in F6Publishing: 5] [Article Influence: 2.6] [Reference Citation Analysis]
20 Dehghan M, Narimani N. Radial basis function-generated finite difference scheme for simulating the brain cancer growth model under radiotherapy in various types of computational domains. Comput Methods Programs Biomed 2020;195:105641. [PMID: 32726719 DOI: 10.1016/j.cmpb.2020.105641] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
21 Pérez-beteta J, Belmonte-beitia J, Pérez-garcía VM, Hubert F. Tumor width on T1-weighted MRI images of glioblastoma as a prognostic biomarker: a mathematical model. Math Model Nat Phenom 2020;15:10. [DOI: 10.1051/mmnp/2019022] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
22 Hormuth DA 2nd, Jarrett AM, Lima EABF, McKenna MT, Fuentes DT, Yankeelov TE. Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data. JCO Clin Cancer Inform 2019;3:1-10. [PMID: 30807209 DOI: 10.1200/CCI.18.00055] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
23 Yankeelov TE, Quaranta V, Evans KJ, Rericha EC. Toward a science of tumor forecasting for clinical oncology. Cancer Res 2015;75:918-23. [PMID: 25592148 DOI: 10.1158/0008-5472.CAN-14-2233] [Cited by in Crossref: 45] [Cited by in F6Publishing: 32] [Article Influence: 6.4] [Reference Citation Analysis]
24 Neufeld Z, von Witt W, Lakatos D, Wang J, Hegedus B, Czirok A. The role of Allee effect in modelling post resection recurrence of glioblastoma. PLoS Comput Biol 2017;13:e1005818. [PMID: 29149169 DOI: 10.1371/journal.pcbi.1005818] [Cited by in Crossref: 26] [Cited by in F6Publishing: 19] [Article Influence: 5.2] [Reference Citation Analysis]
25 Benzekry S, Lamont C, Beheshti A, Tracz A, Ebos JM, Hlatky L, Hahnfeldt P. Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol 2014;10:e1003800. [PMID: 25167199 DOI: 10.1371/journal.pcbi.1003800] [Cited by in Crossref: 257] [Cited by in F6Publishing: 168] [Article Influence: 32.1] [Reference Citation Analysis]
26 Mahlbacher GE, Reihmer KC, Frieboes HB. Mathematical modeling of tumor-immune cell interactions. J Theor Biol 2019;469:47-60. [PMID: 30836073 DOI: 10.1016/j.jtbi.2019.03.002] [Cited by in Crossref: 36] [Cited by in F6Publishing: 17] [Article Influence: 12.0] [Reference Citation Analysis]
27 Jarrett AM, Lima EABF, Hormuth DA 2nd, McKenna MT, Feng X, Ekrut DA, Resende ACM, Brock A, Yankeelov TE. Mathematical models of tumor cell proliferation: A review of the literature. Expert Rev Anticancer Ther 2018;18:1271-86. [PMID: 30252552 DOI: 10.1080/14737140.2018.1527689] [Cited by in Crossref: 28] [Cited by in F6Publishing: 21] [Article Influence: 7.0] [Reference Citation Analysis]
28 Setty Y. eBrain: a Three Dimensional Simulation Tool to Study Drug Delivery in the Brain. Sci Rep 2019;9:6162. [PMID: 30992468 DOI: 10.1038/s41598-019-42261-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
29 Jarrett AM, Hormuth DA 2nd, Wu C, Kazerouni AS, Ekrut DA, Virostko J, Sorace AG, DiCarlo JC, Kowalski J, Patt D, Goodgame B, Avery S, Yankeelov TE. Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data. Neoplasia 2020;22:820-30. [PMID: 33197744 DOI: 10.1016/j.neo.2020.10.011] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
30 Rockne RC, Hawkins-Daarud A, Swanson KR, Sluka JP, Glazier JA, Macklin P, Hormuth DA, Jarrett AM, Lima EABF, Tinsley Oden J, Biros G, Yankeelov TE, Curtius K, Al Bakir I, Wodarz D, Komarova N, Aparicio L, Bordyuh M, Rabadan R, Finley SD, Enderling H, Caudell J, Moros EG, Anderson ARA, Gatenby RA, Kaznatcheev A, Jeavons P, Krishnan N, Pelesko J, Wadhwa RR, Yoon N, Nichol D, Marusyk A, Hinczewski M, Scott JG. The 2019 mathematical oncology roadmap. Phys Biol 2019;16:041005. [PMID: 30991381 DOI: 10.1088/1478-3975/ab1a09] [Cited by in Crossref: 64] [Cited by in F6Publishing: 43] [Article Influence: 21.3] [Reference Citation Analysis]
31 Stamatakos GS, Giatili SG. A Numerical Handling of the Boundary Conditions Imposed by the Skull on an Inhomogeneous Diffusion-Reaction Model of Glioblastoma Invasion Into the Brain: Clinical Validation Aspects. Cancer Inform 2017;16:1176935116684824. [PMID: 28469383 DOI: 10.1177/1176935116684824] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
32 Hunt A, Surulescu C. A Multiscale Modeling Approach to Glioma Invasion with Therapy. Vietnam J Math 2017;45:221-40. [DOI: 10.1007/s10013-016-0223-x] [Cited by in Crossref: 14] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
33 Dejaegher J, Van Gool S, De Vleeschouwer S. Dendritic cell vaccination for glioblastoma multiforme: review with focus on predictive factors for treatment response. Immunotargets Ther 2014;3:55-66. [PMID: 27471700 DOI: 10.2147/ITT.S40121] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.4] [Reference Citation Analysis]
34 Johnston SK, Whitmire P, Massey SC, Kumthekar P, Porter AB, Raghunand N, Gonzalez-Cuyar LF, Mrugala MM, Hawkins-Daarud A, Jackson PR, Hu LS, Sarkaria JN, Wang L, Gatenby RA, Egan KM, Canoll P, Swanson KR; ENDURES consortium. ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: A Multidisciplinary Team-based, Multifactorial Analytical Approach. Am J Clin Oncol 2019;42:655-61. [PMID: 31343422 DOI: 10.1097/COC.0000000000000564] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
35 Collis J, Connor AJ, Paczkowski M, Kannan P, Pitt-francis J, Byrne HM, Hubbard ME. Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial. Bull Math Biol 2017;79:939-74. [DOI: 10.1007/s11538-017-0258-5] [Cited by in Crossref: 23] [Cited by in F6Publishing: 9] [Article Influence: 4.6] [Reference Citation Analysis]
36 Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide. Computer Methods and Programs in Biomedicine 2020;193:105443. [DOI: 10.1016/j.cmpb.2020.105443] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
37 Alfonso JC, Köhn-Luque A, Stylianopoulos T, Feuerhake F, Deutsch A, Hatzikirou H. Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights. Sci Rep 2016;6:37283. [PMID: 27876890 DOI: 10.1038/srep37283] [Cited by in Crossref: 31] [Cited by in F6Publishing: 18] [Article Influence: 5.2] [Reference Citation Analysis]
38 Enderling H, Rejniak KA. Simulating cancer: computational models in oncology. Front Oncol 2013;3:233. [PMID: 24062986 DOI: 10.3389/fonc.2013.00233] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 1.1] [Reference Citation Analysis]
39 Oraiopoulou ME, Tzamali E, Tzedakis G, Vakis A, Papamatheakis J, Sakkalis V. In Vitro/In Silico Study on the Role of Doubling Time Heterogeneity among Primary Glioblastoma Cell Lines. Biomed Res Int 2017;2017:8569328. [PMID: 29226151 DOI: 10.1155/2017/8569328] [Cited by in Crossref: 18] [Cited by in F6Publishing: 12] [Article Influence: 3.6] [Reference Citation Analysis]
40 Jacobs JJ, Capek S, Spinner RJ, Swanson KR. Mathematical model of perineural tumor spread: a pilot study. Acta Neurochir (Wien) 2018;160:655-61. [PMID: 29264779 DOI: 10.1007/s00701-017-3423-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
41 Protopapa M, Zygogianni A, Stamatakos GS, Antypas C, Armpilia C, Uzunoglu NK, Kouloulias V. Clinical implications of in silico mathematical modeling for glioblastoma: a critical review. J Neurooncol 2018;136:1-11. [PMID: 29081039 DOI: 10.1007/s11060-017-2650-2] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 1.6] [Reference Citation Analysis]
42 Hormuth DA 2nd, Weis JA, Barnes SL, Miga MI, Quaranta V, Yankeelov TE. Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer. Int J Radiat Oncol Biol Phys 2018;100:1270-9. [PMID: 29398129 DOI: 10.1016/j.ijrobp.2017.12.004] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 2.2] [Reference Citation Analysis]
43 Chen Y, Lowengrub JS. Tumor growth in complex, evolving microenvironmental geometries: a diffuse domain approach. J Theor Biol 2014;361:14-30. [PMID: 25014472 DOI: 10.1016/j.jtbi.2014.06.024] [Cited by in Crossref: 12] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
44 Hormuth DA 2nd, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022;187:114367. [PMID: 35654212 DOI: 10.1016/j.addr.2022.114367] [Reference Citation Analysis]
45 Stein S, Zhao R, Haeno H, Vivanco I, Michor F. Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients. PLoS Comput Biol 2018;14:e1005924. [PMID: 29293494 DOI: 10.1371/journal.pcbi.1005924] [Cited by in Crossref: 24] [Cited by in F6Publishing: 16] [Article Influence: 6.0] [Reference Citation Analysis]
46 Rayfield CA, Grady F, De Leon G, Rockne R, Carrasco E, Jackson P, Vora M, Johnston SK, Hawkins-Daarud A, Clark-Swanson KR, Whitmire S, Gamez ME, Porter A, Hu L, Gonzalez-Cuyar L, Bendok B, Vora S, Swanson KR. Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival. JCO Clin Cancer Inform 2018;2:1-14. [PMID: 30652553 DOI: 10.1200/CCI.17.00080] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
47 Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA 2nd, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021;16:5309-38. [PMID: 34552262 DOI: 10.1038/s41596-021-00617-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
48 Karolak A, Markov DA, McCawley LJ, Rejniak KA. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 2018;15:20170703. [PMID: 29367239 DOI: 10.1098/rsif.2017.0703] [Cited by in Crossref: 52] [Cited by in F6Publishing: 40] [Article Influence: 17.3] [Reference Citation Analysis]
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