[1] F. Biemar, M. Foti, Global progress against cancer-challenges and opportunities, Cancer Biology and Medicine, vol. 10, no. 4, pp. 183–186, 2013.
[2] R. P. Araujo, D. L. S. McElwain, A history of the study of solid tumour growth: the contribution of mathematical modelling, Bulletin of Mathematical Biology, vol. 66, pp. 1039–1091, 2004.
[3] J. Poleszczuk, P. Hahnfeldt, H. Enderling, Therapeutic implications from sensitivity analysis of tumor angiogenesis models, PLoS One, vol. 10, no. 3, p. 0120007, 2015.
[4] U. Ledzewicz, H. Schättler, A. Friedman, E. Kashdan, Mathematical methods and models in biomedicine, Springer, 2012.
[5] J. C. Doloff, D. J. Waxman, Transcriptional profiling provides insights into metronomic cyclophosphamide activated innate immune-dependent regression of brain tumor xenografts, BMC Cancer, vol. 15, p. 375, 2015.
[6] F. F. Teles, J. M. Lemos, Cancer therapy optimization based on multiple model adaptive control, Biomedical Signal Processing and Control, vol. 48, pp. 255–264, 2019.
[7] R. A. Gatenby, J. S. Brown, Integrating evolutionary dynamics into cancer therapy, Nature Reviews Clinical Oncology, vol. 17, no. 11, pp. 675–686, 2020.
[8] A. Hernandez-Rivera, P. Velarde, A. Zafra-Cabeza, J. M. Maestre, Optimal drug administration in cancer therapy using stochastic non-linear model predictive control, European Control Conference, Stockholm, 2024.
[9] A. Bukkuri, Optimal control analysis of combined chemotherapy-immunotherapy treatment regimens in a PKPD cancer evolution model, Biomath, vol. 9, no. 1, p. 2002137, 2020.
[10] N. H. Sweilam, S. M. Al-Mekhlafi, T. Assiri, A. Atangana, Optimal control for cancer treatment mathematical model using Atangana–Baleanu–Caputo fractional derivative, Advances in Difference Equations, 2020, DOI: https://doi.org/10.1186/s13662-020-02793-9.
[11] E. Ioannidou, M. Moschetta, S. Shah, et al., Angiogenesis and anti-angiogenic treatment in prostate cancer: Mechanisms of action and molecular targets, International Journal of Molecular Sciences, vol. 22, p. 9926, 2021.
[12] M. Conti, S. Gatti, A. Miranville, Mathematical analysis of a phase-field model of brain cancers with chemotherapy and antiangiogenic therapy effects, AIMS Mathematics, vol. 7, no. 1, pp. 1536–1561, 2021.
[13] F. Lopes-Coelho, F. Martins, S. A. Pereira, J. Serpa, Anti-angiogenic therapy: current challenges and future perspectives, International Journal of Molecular Sciences, vol. 22, p. 3765, 2021.
[14] M. J. Ansari, D. Bokov, A. Markov, et al., Cancer combination therapies by angiogenesis inhibitors: a comprehensive review, Cell Communication and Signaling, vol. 20, no. 1, 2022.
[15] M. Y. Wang. J. G. Scott, A. Vladimirsk, Stochastic optimal control to guide adaptive cancer therapy, BioRxiv, 2022, DOI: https://doi.org/10.1101/2022.06.17.496649.
[16] J. Kohler, M. N. Zeilinger, Predictive control for nonlinear stochastic systems: closed-loop guarantees with unbounded noise, IEEE Transactions on Automatic Control, 2024, DOI: 10.1109/TAC.2025.3571575.
[17] S. A. N. Nouwens, B. de Jager, M. M. Paulides, W. P. M. H. Heemels, Constraint removal for MPC with performance preservation and a hyperthermia cancer treatment case study, IEEE Conference on Decision and Control, Austin, 2021.
[18] S. Belkhir, F. Thomas, B. Roche, Darwinian approaches for cancer treatment: benefits of mathematical modeling, Cancers, vol. 13, no. 17, p. 4448, 2021.
[19] S. M. Tu, C. C. Guo, D. S. L. Chow, N. M. Zacharias, Stem cell theory of cancer: implications for drug resistance and chemosensitivity in cancer care, Cancers, vol. 14, no. 6, p. 1548, 2022.
[20] F. Angaroni, A. Graudenzi, M. Rossignolo, D. Maspero, T. Calarco, An optimal control framework for the automated design of personalized cancer treatments, Frontiers in Bioengineering and Biotechnology, 2020.
[21] B. Smart, I. D. Cesare, L. Renson, L. Marucci, Model predictive control of cancer cellular dynamics: a new strategy for therapy design, Frontiers in Control Engineering, vol. 3, 2022.
[22] M. Gluzman, J. G. Scott, A. Vladimirsk, Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory, Proceedings of the Royal Society B, vol. 287, no. 1925, p. 20192454, 2020.
[23] S. T. R. Pinho, F. S. Bacelar, R. F. S. Andrade, H. I. Freedman, A mathematical model for the effect of anti-angiogenic therapy in the treatment of cancer tumors by chemotherapy, Nonlinear Analysis: Real World Applications, vol. 14, no. 1, pp. 815–828, 2013.
[24] E. A. Sarapata, L. G. De Pillis, A comparison and catalog of intrinsic tumor growth models, Bulletin of Mathematical Biology, vol. 76, no. 8, pp. 2010–2024, 2014.
[25] S. Ragusa, B. Prat-Luri, A. González-Loyola, et al., Antiangiogenic immunotherapy suppresses desmoplastic and chemo resistant intestinal tumors in mice, The Journal of Clinical Investigation, vol. 130, pp. 1199-1216, 2020.
[26] P. Khalili, S. Zolatash, R. Vatankhah, S. Taghvaei, Optimal control methods for drug delivery in cancerous tumour by anti-angiogenic therapy and chemotherapy, IET Systems Biology, vol. 15, no. 1, pp. 14-25, 2021.
