Open Access
Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
---|---|---|
Article Number | 00118 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/bioconf/20249700118 | |
Published online | 05 April 2024 |
- Hanly, P., Pearce, A. & Sharp, L. The cost of premature cancer-related mortality: a review and assessment of the evidence. Expert Rev. Pharmacoecon. Outcomes Res. 14, 355–377 (2014). [CrossRef] [PubMed] [Google Scholar]
- Schmitz, K.H., DiSipio, T., Gordon, L.G. & Hayes, S.C. Adverse breast cancer treatment effects: the economic case for making rehabilitative programs standard of care. Support. Care Cancer Off. J. Multinatl. Assoc. Support. Care Cancer 23, 1807–1817 (2015). [Google Scholar]
- Carlotto, A., Hogsett, V.L., Maiorini, E.M., Razulis, J.G. & Sonis, S.T. The Economic Burden of Toxicities Associated with Cancer Treatment: Review of the Literature and Analysis of Nausea and Vomiting, Diarrhoea, Oral Mucositis and Fatigue. PharmacoEconomics 31, 753–766 (2013). [CrossRef] [PubMed] [Google Scholar]
- Glynn, R.W., Chin, J.Z., Kerin, M.J. & Sweeney, K.J. Representation of Cancer in the Medical Literature - A Bibliometric Analysis. PLOS ONE 5, e13902 (2010). [CrossRef] [PubMed] [Google Scholar]
- The Combined Therapeutical Effect of Metal-based Drugs and Radiation Therapy: The Present Status of Research | Bentham Science. https://www.eurekaselect.com/article/59210. [Google Scholar]
- Bakhshinejad, B., Karimi, M. & Sadeghizadeh, M. Bacteriophages and medical oncology: targeted gene therapy of cancer. Med. Oncol. 31, 110 (2014). [CrossRef] [PubMed] [Google Scholar]
- Kuroki, M. & Shirasu, N. Novel treatment strategies for cancer and their tumor-targeting approaches using antibodies against tumor-associated antigens. Anticancer Res. 34, 4481–4488 (2014). [Google Scholar]
- Personalizing oncology treatments by predicting drug efficacy, side‐effects, and improved therapy: mathematics, statistics, and their integration - Agur - 2014 - WIREs Systems Biology and Medicine - Wiley Online Library. https://wires.onlinelibrary.wiley.com/doi/10.1002/wsbm.1263. [Google Scholar]
- Dynamics of Tumour Growth - PMC. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2071101/. [Google Scholar]
- Dethlefsen, L.A., Prewitt, J.M. & Mendelsohn, M.L. Analysis of tumor growth curves. J. Natl. Cancer Inst. 40, 389–405 (1968). [CrossRef] [PubMed] [Google Scholar]
- Brodin, N.P. et al. Optimizing the radiation therapy dose prescription for pediatric medulloblastoma: Minimizing the life years lost attributable to failure to control the disease and late complication risk. Acta Oncol. 53, 462–470 (2014). [CrossRef] [PubMed] [Google Scholar]
- Batmani, Y. & Khaloozadeh, H. Optimal drug regimens in cancer chemotherapy: a multi-objective approach. Comput. Biol. Med. 43, 2089–2095 (2013). [CrossRef] [Google Scholar]
- Agur, Z., Elishmereni, M. & Kheifetz, Y. Personalizing oncology treatments by predicting drug efficacy, sideeffects, and improved therapy: mathematics, statistics, and their integration. Wiley Interdiscip. Rev. Syst. Biol. Med. 6, 239–253 (2014). [CrossRef] [PubMed] [Google Scholar]
- Huang, X., Ning, J. & Wahed, A.S. Optimization of individualized dynamic treatment regimes for recurrent diseases. Stat. Med. 33, 2363–2378 (2014). [CrossRef] [PubMed] [Google Scholar]
- Moodie, E.E.M., Richardson, T.S. & Stephens, D.A. Demystifying optimal dynamic treatment regimes. Biometrics 63, 447–455 (2007). [CrossRef] [PubMed] [Google Scholar]
- Wang, Z. & Deisboeck, T.S. Mathematical modeling in cancer drug discovery. Drug Discov. Today 19, 145–150 (2014). [CrossRef] [Google Scholar]
- Panetta, J.C. A mathematical model of drug resistance: heterogeneous tumors. Math. Biosci. 147, 41–61 (1998). [CrossRef] [Google Scholar]
- Sakode, C.M., Padhi, R., Kapoor, S., Rallabandi, V.P.S. & Roy, P.K. Multimodal therapy for complete regression of malignant melanoma using constrained nonlinear optimal dynamic inversion. Biomed. Signal Process. Control 13, 198–211 (2014). [CrossRef] [Google Scholar]
- Worschech, A. et al. Systemic treatment of xenografts with vaccinia virus GLV-1h68 reveals the immunologic facet of oncolytic therapy. BMC Genomics 10, 301 (2009). [CrossRef] [PubMed] [Google Scholar]
- Gerlee, P. The Model Muddle: In Search of Tumor Growth Laws. Cancer Res. 73, 2407–2411 (2013). [CrossRef] [PubMed] [Google Scholar]
- Wodarz, D. & Komarova, N. Towards Predictive Computational Models of Oncolytic Virus Therapy: Basis for Experimental Validation and Model Selection. PLOS ONE 4, e4271 (2009). [CrossRef] [PubMed] [Google Scholar]
- Murphy, H., Jaafari, H. & Dobrovolny, H.M. Differences in predictions of ODE models of tumor growth: a cautionary example. BMC Cancer 16, 163 (2016). [CrossRef] [PubMed] [Google Scholar]
- Vaidya, V.G. & Alexandro, F.J. Evaluation of some mathematical models for tumor growth. Int. J. Biomed. Comput. 13, 19–36 (1982). [CrossRef] [Google Scholar]
- Sarapata, E.A. & de Pillis, L.G. A Comparison and Catalog of Intrinsic Tumor Growth Models. Bull. Math. Biol. 76, 2010–2024 (2014). [CrossRef] [PubMed] [Google Scholar]
- Benzekry, S. et al. Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput. Biol. 10, e1003800 (2014). [CrossRef] [Google Scholar]
- Hartung, N. et al. Mathematical modeling of tumor growth and metastatic spreading: validation in tumor-bearing mice. Cancer Res. 74, 6397–6407 (2014). [CrossRef] [PubMed] [Google Scholar]
- Sarapata, E.A. & de Pillis, L.G. A comparison and catalog of intrinsic tumor growth models. Bull. Math. Biol. 76, 2010–2024 (2014). [CrossRef] [PubMed] [Google Scholar]
- Dynamics of Cancer: Mathematical Foundations of Oncology: 9789814566360: Medicine & Health Science Books @ Amazon.com. https://www.amazon.com/Dynamics-Cancer-Mathematical-Foundations-Oncology/dp/9814566365. [Google Scholar]
- Collins, V.P., Loeffler, R.K. & Tivey, H. Observations on growth rates of human tumors. Am. J. Roentgenol. Radium Ther. Nucl. Med. 76, 988–1000 (1956). [Google Scholar]
- Verhulst, P.F. (1838) Notice sur la loi que la population suit dans son accroissement. Correspondence Mathematique et Physique (Ghent), 10, 113–121. - References - Scientific Research Publishing. https://www.scirp.org/reference/referencespapers?referenceid=2399035. [Google Scholar]
- Winsor, C.P. The Gompertz Curve as a Growth Curve. Proc. Natl. Acad. Sci. 18, 1–8 (1932). [CrossRef] [PubMed] [Google Scholar]
- Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth | PLOS Computational Biology. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003800. [Google Scholar]
- An Emerging Allee Effect Is Critical for Tumor Initiation and Persistence - PMC. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4559422/. [Google Scholar]
- Bertalanffy, L. Problems of organic growth. Nature 163, 156–158 (1949). [CrossRef] [PubMed] [Google Scholar]
- Introduction to Mathematical Oncology - 1st Edition - Yang Kuang - Joh. https://www.routledge.com/Introduction-to-Mathematical-Oncology/Kuang-Nagy-Eikenberry/p/book/9780367783150#. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.