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For: Corwin D, Holdsworth C, Rockne RC, Trister AD, Mrugala MM, Rockhill JK, Stewart RD, Phillips M, Swanson KR. Toward patient-specific, biologically optimized radiation therapy plans for the treatment of glioblastoma. PLoS One 2013;8:e79115. [PMID: 24265748 DOI: 10.1371/journal.pone.0079115] [Cited by in Crossref: 77] [Cited by in F6Publishing: 59] [Article Influence: 8.6] [Reference Citation Analysis]
Number Citing Articles
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3 Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. Journal of Biomedical Informatics 2022. [DOI: 10.1016/j.jbi.2022.104006] [Reference Citation Analysis]
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6 Hawkins-Daarud A, Rockne R, Corwin D, Anderson AR, Kinahan P, Swanson KR. In silico analysis suggests differential response to bevacizumab and radiation combination therapy in newly diagnosed glioblastoma. J R Soc Interface 2015;12:20150388. [PMID: 26202682 DOI: 10.1098/rsif.2015.0388] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 1.7] [Reference Citation Analysis]
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8 Colli P, Gomez H, Lorenzo G, Marinoschi G, Reali A, Rocca E. Optimal control of cytotoxic and antiangiogenic therapies on prostate cancer growth. Math Models Methods Appl Sci 2021;31:1419-68. [DOI: 10.1142/s0218202521500299] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
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10 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]
11 Hathout L, Pope WB, Lai A, Nghiemphu PL, Cloughesy TF, Ellingson BM. Radial expansion rates and tumor growth kinetics predict malignant transformation in contrast-enhancing low-grade diffuse astrocytoma. CNS Oncol 2015;4:247-56. [PMID: 26095141 DOI: 10.2217/cns.15.16] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 1.6] [Reference Citation Analysis]
12 Agosti A, Giverso C, Faggiano E, Stamm A, Ciarletta P. A personalized mathematical tool for neuro-oncology: A clinical case study. International Journal of Non-Linear Mechanics 2018;107:170-81. [DOI: 10.1016/j.ijnonlinmec.2018.06.004] [Cited by in Crossref: 10] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
13 Elazab A, Anter AM, Bai H, Hu Q, Hussain Z, Ni D, Wang T, Lei B. An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects. Applied Soft Computing 2019;80:617-27. [DOI: 10.1016/j.asoc.2019.04.034] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Swan A, Hillen T, Bowman JC, Murtha AD. A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread. Bull Math Biol 2018;80:1259-91. [DOI: 10.1007/s11538-017-0271-8] [Cited by in Crossref: 27] [Cited by in F6Publishing: 16] [Article Influence: 5.4] [Reference Citation Analysis]
15 Jacob J, Durand T, Feuvret L, Mazeron JJ, Delattre JY, Hoang-Xuan K, Psimaras D, Douzane H, Ribeiro M, Capelle L, Carpentier A, Ricard D, Maingon P. Cognitive impairment and morphological changes after radiation therapy in brain tumors: A review. Radiother Oncol 2018;128:221-8. [PMID: 30041961 DOI: 10.1016/j.radonc.2018.05.027] [Cited by in Crossref: 25] [Cited by in F6Publishing: 20] [Article Influence: 6.3] [Reference Citation Analysis]
16 Massey SC, White H, Whitmire P, Doyle T, Johnston SK, Singleton KW, Jackson PR, Hawkins-Daarud A, Bendok BR, Porter AB, Vora S, Sarkaria JN, Hu LS, Mrugala MM, Swanson KR. Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma. PLoS One 2020;15:e0230492. [PMID: 32218600 DOI: 10.1371/journal.pone.0230492] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
17 Lee HG, Kim Y. The role of the microenvironment in regulation of CSPG-driven invasive and non-invasive tumor growth in glioblastoma: Brain tumor growth. Japan J Indust Appl Math 2015;32:771-805. [DOI: 10.1007/s13160-015-0188-2] [Cited by in Crossref: 3] [Article Influence: 0.4] [Reference Citation Analysis]
18 Lorenzo G, Scott MA, Tew K, Hughes TJ, Zhang YJ, Liu L, Vilanova G, Gomez H. Tissue-scale, personalized modeling and simulation of prostate cancer growth. Proc Natl Acad Sci U S A 2016;113:E7663-71. [PMID: 27856758 DOI: 10.1073/pnas.1615791113] [Cited by in Crossref: 39] [Cited by in F6Publishing: 15] [Article Influence: 6.5] [Reference Citation Analysis]
19 Hormuth DA 2nd, Jarrett AM, Davis T, Yankeelov TE. Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy. Cancers (Basel) 2021;13:1765. [PMID: 33917080 DOI: 10.3390/cancers13081765] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [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 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]
22 Post CCB, Kramer MCA, Smid EJ, van der Weide HL, Kleynen CE, Heesters MAAM, Verhoeff JJC. Patterns of re-irradiation for recurrent gliomas and validation of a prognostic score. Radiother Oncol 2019;130:156-63. [PMID: 30446315 DOI: 10.1016/j.radonc.2018.10.034] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]
23 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]
24 Nie XH, Ou-yang J, Xing Y, Li DY, Dong XY, Liu RE, Xu RX. Paeoniflorin inhibits human glioma cells via STAT3 degradation by the ubiquitin-proteasome pathway. Drug Des Devel Ther 2015;9:5611-22. [PMID: 26508835 DOI: 10.2147/DDDT.S93912] [Cited by in Crossref: 10] [Cited by in F6Publishing: 19] [Article Influence: 1.4] [Reference Citation Analysis]
25 Grassberger C, Paganetti H. Methodologies in the modeling of combined chemo-radiation treatments. Phys Med Biol 2016;61:R344-67. [DOI: 10.1088/0031-9155/61/21/r344] [Cited by in Crossref: 11] [Cited by in F6Publishing: 4] [Article Influence: 1.8] [Reference Citation Analysis]
26 Enderling H, Alfonso JCL, Moros E, Caudell JJ, Harrison LB. Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy. Trends Cancer 2019;5:467-74. [PMID: 31421904 DOI: 10.1016/j.trecan.2019.06.006] [Cited by in Crossref: 17] [Cited by in F6Publishing: 13] [Article Influence: 5.7] [Reference Citation Analysis]
27 Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G. Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology. Annu Rev Biomed Eng 2020;22:309-41. [PMID: 32501772 DOI: 10.1146/annurev-bioeng-062117-121105] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
28 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]
29 Elazab A, Bai H, Abdulazeem YM, Abdelhamid T, Zhou S, Wong KKL, Hu Q. Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment. Sci Rep 2017;7:1222. [PMID: 28450707 DOI: 10.1038/s41598-017-01189-2] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Article Influence: 2.2] [Reference Citation Analysis]
30 Amelot A, Stretton E, Delingette H, Ayache N, Froelich S, Mandonnet E. Expert-validated CSF segmentation of MNI atlas enhances accuracy of virtual glioma growth patterns. J Neurooncol 2015;121:381-7. [PMID: 25370706 DOI: 10.1007/s11060-014-1645-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
31 Patel V, Hathout L. Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme. Theor Biol Med Model 2017;14:10. [PMID: 28464925 DOI: 10.1186/s12976-017-0056-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
32 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]
33 Storey K, Leder K, Hawkins-Daarud A, Swanson K, Ahmed AU, Rockne RC, Foo J. Glioblastoma Recurrence and the Role of O6-Methylguanine-DNA Methyltransferase Promoter Methylation. JCO Clin Cancer Inform 2019;3:1-12. [PMID: 30758983 DOI: 10.1200/CCI.18.00062] [Cited by in Crossref: 21] [Cited by in F6Publishing: 7] [Article Influence: 10.5] [Reference Citation Analysis]
34 Altrock PM, Liu LL, Michor F. The mathematics of cancer: integrating quantitative models. Nat Rev Cancer 2015;15:730-45. [PMID: 26597528 DOI: 10.1038/nrc4029] [Cited by in Crossref: 354] [Cited by in F6Publishing: 210] [Article Influence: 59.0] [Reference Citation Analysis]
35 Massey SC, Urcuyo JC, Marin BM, Sarkaria JN, Swanson KR. Quantifying Glioblastoma Drug Response Dynamics Incorporating Treatment Sensitivity and Blood Brain Barrier Penetrance From Experimental Data. Front Physiol 2020;11:830. [PMID: 32973540 DOI: 10.3389/fphys.2020.00830] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
36 Whitmire P, Rickertsen CR, Hawkins-Daarud A, Carrasco E Jr, Lorence J, De Leon G, Curtin L, Bayless S, Clark-Swanson K, Peeri NC, Corpuz C, Lewis-de Los Angeles CP, Bendok BR, Gonzalez-Cuyar L, Vora S, Mrugala MM, Hu LS, Wang L, Porter A, Kumthekar P, Johnston SK, Egan KM, Gatenby R, Canoll P, Rubin JB, Swanson KR. Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients. BMC Cancer 2020;20:447. [PMID: 32429869 DOI: 10.1186/s12885-020-06816-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
37 Kim Y, Lee D, Lee J, Lee S, Lawler S. Role of tumor-associated neutrophils in regulation of tumor growth in lung cancer development: A mathematical model. PLoS One 2019;14:e0211041. [PMID: 30689655 DOI: 10.1371/journal.pone.0211041] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 3.7] [Reference Citation Analysis]
38 Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021;115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
39 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]
40 Lee J, Lee D, Kim Y. Mathematical model of STAT signalling pathways in cancer development and optimal control approaches. R Soc Open Sci 2021;8:210594. [PMID: 34631119 DOI: 10.1098/rsos.210594] [Reference Citation Analysis]
41 Rechner LA, Modiri A, Stick LB, Maraldo MV, Aznar MC, Rice SR, Sawant A, Bentzen SM, Vogelius IR, Specht L. Biological optimization for mediastinal lymphoma radiotherapy - a preliminary study. Acta Oncol 2020;59:879-87. [PMID: 32216586 DOI: 10.1080/0284186X.2020.1733654] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
42 Sunassee ED, Tan D, Ji N, Brady R, Moros EG, Caudell JJ, Yartsev S, Enderling H. Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses. Int J Radiat Biol 2019;95:1421-6. [PMID: 30831050 DOI: 10.1080/09553002.2019.1589013] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
43 Colli P, Gomez H, Lorenzo G, Marinoschi G, Reali A, Rocca E. Mathematical analysis and simulation study of a phase-field model of prostate cancer growth with chemotherapy and antiangiogenic therapy effects. Math Models Methods Appl Sci 2020;30:1253-95. [DOI: 10.1142/s0218202520500220] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 3.5] [Reference Citation Analysis]
44 Lee J, Kim JS, Kim Y. Atorvastatin-mediated rescue of cancer-related cognitive changes in combined anticancer therapies. PLoS Comput Biol 2021;17:e1009457. [PMID: 34669701 DOI: 10.1371/journal.pcbi.1009457] [Reference Citation Analysis]
45 Kalpathy-Cramer J, Gerstner ER, Emblem KE, Andronesi O, Rosen B. Advanced magnetic resonance imaging of the physical processes in human glioblastoma. Cancer Res 2014;74:4622-37. [PMID: 25183787 DOI: 10.1158/0008-5472.CAN-14-0383] [Cited by in Crossref: 83] [Cited by in F6Publishing: 57] [Article Influence: 11.9] [Reference Citation Analysis]
46 Lorenzo G, Pérez-García VM, Mariño A, Pérez-Romasanta LA, Reali A, Gomez H. Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome. J R Soc Interface 2019;16:20190195. [PMID: 31409240 DOI: 10.1098/rsif.2019.0195] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 3.3] [Reference Citation Analysis]
47 Jarrett AM, Hormuth DA, Barnes SL, Feng X, Huang W, Yankeelov TE. Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results. Phys Med Biol 2018;63:105015. [PMID: 29697054 DOI: 10.1088/1361-6560/aac040] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 4.5] [Reference Citation Analysis]
48 Borasi G, Nahum A, Paulides MM, Powathil G, Russo G, Fariselli L, Lamia D, Cirincione R, Forte GI, Borrazzo C, Caccia B, di Castro E, Pozzi S, Gilardi MC. Fast and high temperature hyperthermia coupled with radiotherapy as a possible new treatment for glioblastoma. J Ther Ultrasound 2016;4:32. [PMID: 27980785 DOI: 10.1186/s40349-016-0078-3] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 1.5] [Reference Citation Analysis]
49 Montaseri G, Alfonso JCL, Hatzikirou H, Meyer-Hermann M. A minimal modeling framework of radiation and immune system synergy to assist radiotherapy planning. J Theor Biol 2020;486:110099. [PMID: 31790681 DOI: 10.1016/j.jtbi.2019.110099] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
50 Hu LS, Yoon H, Eschbacher JM, Baxter LC, Dueck AC, Nespodzany A, Smith KA, Nakaji P, Xu Y, Wang L, Karis JP, Hawkins-Daarud AJ, Singleton KW, Jackson PR, Anderies BJ, Bendok BR, Zimmerman RS, Quarles C, Porter-Umphrey AB, Mrugala MM, Sharma A, Hoxworth JM, Sattur MG, Sanai N, Koulemberis PE, Krishna C, Mitchell JR, Wu T, Tran NL, Swanson KR, Li J. Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning. AJNR Am J Neuroradiol 2019;40:418-25. [PMID: 30819771 DOI: 10.3174/ajnr.A5981] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 2.7] [Reference Citation Analysis]
51 Rockne R, Rockhill JK, Mrugala M, Spence AM, Kalet I, Hendrickson K, Lai A, Cloughesy T, Alvord EC, Swanson KR. Reply to comment on: ‘Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo : a mathematical modeling approach’. Phys Med Biol 2016;61:2968-9. [DOI: 10.1088/0031-9155/61/7/2968] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
52 Scott JG, Fletcher AG, Anderson AR, Maini PK. Spatial Metrics of Tumour Vascular Organisation Predict Radiation Efficacy in a Computational Model. PLoS Comput Biol. 2016;12:e1004712. [PMID: 26800503 DOI: 10.1371/journal.pcbi.1004712] [Cited by in Crossref: 27] [Cited by in F6Publishing: 22] [Article Influence: 4.5] [Reference Citation Analysis]
53 Jacobs J, Rockne RC, Hawkins-Daarud AJ, Jackson PR, Johnston SK, Kinahan P, Swanson KR. Improved model prediction of glioma growth utilizing tissue-specific boundary effects. Math Biosci 2019;312:59-66. [PMID: 31009624 DOI: 10.1016/j.mbs.2019.04.004] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
54 Ferrandon S, Magné N, Battiston-Montagne P, Hau-Desbat NH, Diaz O, Beuve M, Constanzo J, Chargari C, Poncet D, Chautard E, Ardail D, Alphonse G, Rodriguez-Lafrasse C. Cellular and molecular portrait of eleven human glioblastoma cell lines under photon and carbon ion irradiation. Cancer Lett 2015;360:10-6. [PMID: 25657111 DOI: 10.1016/j.canlet.2015.01.025] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 1.9] [Reference Citation Analysis]
55 Bae IS, Kim CH, Kim JM, Cheong JH, Ryu JI, Han MH. Correlation of survivin and B-cell lymphoma 2 expression with pathological malignancy and anti-apoptotic properties of glial cell tumors. Biomed Rep 2017;6:396-400. [PMID: 28413637 DOI: 10.3892/br.2017.861] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
56 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]
57 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]
58 Jarrett AM, Faghihi D, Ii DAH, Lima EABF, Virostko J, Biros G, Patt D, Yankeelov TE. Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities. J Clin Med 2020;9:E1314. [PMID: 32370195 DOI: 10.3390/jcm9051314] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
59 Borasi G. Comment on ‘Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo : a mathematical modeling approach’. Phys Med Biol 2016;61:2967-2967. [DOI: 10.1088/0031-9155/61/7/2967] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
60 Nourollahi S, Ghate A, Kim M. Optimal modality selection in external beam radiotherapy. Math Med Biol 2019;36:361-80. [PMID: 30192934 DOI: 10.1093/imammb/dqy013] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
61 Hathout L, Ellingson B, Pope W. Modeling the efficacy of the extent of surgical resection in the setting of radiation therapy for glioblastoma. Cancer Sci 2016;107:1110-6. [PMID: 27240229 DOI: 10.1111/cas.12979] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 1.8] [Reference Citation Analysis]
62 Hatzikirou H, Alfonso JC, Mühle S, Stern C, Weiss S, Meyer-Hermann M. Cancer therapeutic potential of combinatorial immuno- and vasomodulatory interventions. J R Soc Interface 2015;12:20150439. [PMID: 26510827 DOI: 10.1098/rsif.2015.0439] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 2.0] [Reference Citation Analysis]
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