**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**32926

##### A Recommendation to Oncologists for Cancer Treatment by Immunotherapy: Quantitative and Qualitative Analysis

**Authors:**
Mandana Kariminejad,
Ali Ghaffari

**Abstract:**

**Keywords:**
Tumor,
immunotherapy,
fuzzy controller,
Genetic
algorithm,
mathematical model.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.3607743

**References:**

[1] Araujo, R.P. and McElwain, D.S., 2004. A history of the study of solid tumour growth: the contribution of mathematical modelling. Bulletin of mathematical biology, 66(5), pp.1039-1091.

[2] Enderling, H., Chaplain, M.A., Anderson, A.R. and Vaidya, J.S., 2007. A mathematical model of breast cancer development, local treatment and recurrence. Journal of theoretical biology, 246(2), pp.245- 259.

[3] Sachs, R.K., Hlatky, L.R. and Hahnfeldt, P., 2001. Simple ODE models of tumor growth and anti- angiogenic or radiation treatment. Mathematical and Computer Modelling, 33(12-13), pp.1297-1305.

[4] Anderson, A.R., Chaplain, M.A., Newman, E.L., Steele, R.J. and Thompson, A.M., 2000. Mathematical modelling of tumour invasion and metastasis. Computational and Mathematical Methods in Medicine, 2(2), pp.129-154.

[5] Swanson, K.R., Bridge, C., Murray, J.D. and Alvord, E.C., 2003. Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. Journal of the neurological sciences, 216(1), pp.1-10.

[6] SzymaĆska, Z., 2003. Analysis of immunotherapy models in the context of cancer dynamics. International Journal of Applied Mathematics and Computer Science, 13(3), pp.407-418.

[7] O'Byrne, K.J., Dalgleish, A.G., Browning, M.J., Steward, W.P. and Harris, A.L., 2000. The relationship between angiogenesis and the immune response in carcinogenesis and the progression of malignant disease. European journal of cancer, 36(2), pp.151-169.

[8] Stewart, T.H., 1996. Immune Mechanisms and Tumor Dormancy. Revista Medicina, 56(1), p.

[9] Restifo, N.P., Dudley, M.E. and Rosenberg, S.A., 2012. Adoptive immunotherapy for cancer: harnessing the T cell response. Nature Reviews Immunology, 12(4), p.269.

[10] de Pillis, L.G., Gu, W. and Radunskaya, A.E., 2006. Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations. Journal of theoretical biology, 238(4), pp.841-862.

[11] Pena-Reyes, C.A. and Sipper, M., 1999. A fuzzy-genetic approach to breast cancer diagnosis. Artificial intelligence in medicine, 17(2), pp.131-155.

[12] Swierniak, A., Kimmel, M. and Smieja, J., 2009. Mathematical modeling as a tool for planning anticancer therapy. European journal of pharmacology, 625(1-3), pp.108-121.

[13] Itik, M., Salamci, M.U. and Banks, S.P., 2010. SDRE optimal control of drug administration in cancer treatment. Turkish Journal of Electrical Engineering & Computer Sciences, 18(5), pp.715- 730.

[14] Burden, T.N., Ernstberger, J. and Fister, K.R., 2004. Optimal control applied to immunotherapy. Discrete and Continuous Dynamical Systems Series B, 4(1), pp.135-146.

[15] Ghaffari, A. and Naserifar, N., 2010. Optimal therapeutic protocols in cancer immunotherapy. Computers in biology and medicine, 40(3), pp.261-270.

[16] Vignard, V., Lemercier, B., Lim, A., Pandolfino, M.C., Guilloux, Y., Khammari, A., Rabu, C., Echasserieau, K., Lang, F., Gougeon, M.L. and Dreno, B., 2005. Adoptive transfer of tumor- reactive Melan-Aspecific CTL clones in melanoma patients is followed by increased frequencies of additional Melan-A-specific T cells. The Journal of Immunology, 175(7), pp.4797-4805.