Overview of mathematical models of breast cancer
https://doi.org/10.24411/2588-0519-2019-10072
Abstract
Rationale. Breast cancer (BC) today is the leading oncologic pathology in Russia. The use of new treatments is associated with high healthcare costs. The epidemiological forecast and cost planning are not possible without the building of the mathematical model of BC.
Aim. The perform a literature review of BC models.
Materials and methods. The systematic literature review was performed by searching databases (PubMed). From 547 initially got publications 20 were included in the analysis. Not included publications could be divided into groups: pharmacoeconomic model of particular drug, BC screening models, model of tumor growth, models of ВС imaging (US, MRI).
Results. BC epidemiologic mathematical model should be based on the patient data from national register, the time horizon should be not less than 5 years, it should be based on Markov modelling and be non-homogenous. The model has to differentiate several tumor types and disease stage.
Conclusion. Today in Russia there is no epidemiologic mathematical model of BC.
About the Authors
Yu. P. Yurkova
FSBI N.N. Petrov National Medical Research Center of Oncology
Russian Federation
Russian Federation
Yurkova Yulia - Medical statistician
SPIN-code: 4697-6433
A. A. Kurylev
FSBEI HE I.P. Pavlov SPbSMU MOH Russia
Russian Federation
Russian Federation
Kurylev Alexey - Assistant of professor Department of Clinical Pharmacology and Evidence-Based Medicine
SPIN-code: 4470-7845
A. S. Kolbin
FSBEI HE I.P. Pavlov SPbSMU MOH Russia; Saint-Petersburg State University
Russian Federation
Russian Federation
Kolbin Alexey - MD, DrSci, Professor, Head of the Department of Clinical Pharmacology and Evidence-Based Medicine, FSBEI HE I.P. Pavlov SPbSMU MOH Russia; professor of the Department of Pharmacology, Medical Faculty St. Petersburg SU
SPIN-code: 7966-0845
References
1. VM. Merabishvili. Epidemiologiya i vyzhivaemost’ bol’nyh rakom molochnoj zhelezy. Voprosy onkologii. 2013;59(3):314-319. (In Russ).
2. VF. Semiglazov Epidemiologiya i skrining raka molochnoj zhelezy Voprosy onkologii. 2017;63(3):375-384. (In Russ).
3. Pod red. AD Kaprina, VV Starinskogo, GV Petrovoj. Sostoyanie onkologicheskoj pomoshchi naseleniyu Rossii v 2017 godu. Moscow. MNIOI im. P.A. Gercena, filial FGBU «NMIC radiologii» Minzdrava Rossii; 2018. (In Russ).
4. Kolbin AS. Nauchnyj analiz iskhodov v onkologii. osobennosti farmakoekonomicheskoj ekspertizy. Medicinskie tekhnologii. Ocenka i vybor. 2012;8(2):87-93. (In Russ).
5. Yang G. Neyman, Markov processes and survival analysis Lifetime Data Anal (2013) 19:393-411. DOI: 10.1007/s10985-013-9250-z
6. Abdollahian M, et al. A MDP model for breast and ovarian cancer intervention strategies for BRCA1/2 mutation carriers. IEEE J Biomed Health Inform. 2015 Mar;19(2):720-7. DOI: 10.1109/JBHI.2014.2319246
7. Beauchemin C, et al. A global economic model to assess the cost-effectiveness of new treatments for advanced breast cancer in Canada. J Med Econ. 2016 Jun;19(6):619-29. DOI: 10.3111/13696998.2016.1151431
8. Buyukdamgaci-Alogan G, et al. A decision-analytic model for early stage breast cancer: lumpectomy vs mastectomy. Neoplasma. 2008;55(3):222-8.
9. Karter KJ, et al. Analysis of Three Decision-making Methods: A Breast Cancer Patient as a Model. Med Decis Making. 1999;19:49-57. DOI: 10.1177/0272989X9901900107
10. Crowther MJ, et al. Parametric multistate survival models: Flexible modelling allowing transition-specific distributions with application to estimating clinically useful measures of effect differences. Stat Med. 2017 Dec 20;36(29):4719-4742.
11. Eulenburg C, et al. A Comprehensive Multistate Model Analyzing Associations of Various Risk Factors With the Course of Breast Cancer in a Population-Based Cohort of Breast Cancer Cases. Am J Epidemiol. 2016 Feb 15;183(4):325-34. DOI: 10.1093/aje/kwv163
12. Genser B, et al. Joint modelling of repeated transitions in follow-up data--a case study on breast cancer data. Biom J. 2005 Jun;47(3):388-401.
13. Holland RR, et al. Life expectancy estimation with breast cancer: bias of the declining exponential function and an alternative to its use. Med Decis Making. 1999 Oct-Dec;19(4):385-93. DOI: 10.1177/0272989X9901900406
14. Hsu CY, et al. Bayesian negative-binomial-family-based multistate Markov model for the evaluation of periodic population-based cancer screening considering incomplete information and measurement errors. Stat Methods Med Res. 2018 Aug;27(8):2519-2539.
15. Hubbard RA, et al. Using semi-Markov processes to study timeliness and tests used in the diagnostic evaluation of suspected breast cancer. Stat Med. 2016 Nov 30;35(27):4980-4993. DOI: 10.1002/sim.7055
16. Hui-Min WuG, et al. A bayesian random-effects markov model for tumor progression in women with a family history of breast cancer. Biometrics. 2008 Dec;64(4):1231-7. DOI: 10.1111/j.1541-0420.2007.00979.x
17. Lange JM, et al. A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data. Biometrics. 2015 Mar;71(1):90-101.
18. Meier-Hirmer C, et al. Multi-state model for studying an intermediate event using time-dependent covariates: application to breast cancer. BMC Med Res Methodol. 2013 Jun 20;13:80. DOI: 10.1186/1471-2288-13-80
19. Pobiruchin M. A method for using real world data in breast cancer modeling. J Biomed Inform. 2016 Apr;60:385-94. DOI: 10.1016/j.jbi.2016.01.017
20. Perez-Ocon R. A piecewise Markov process for analysing survival from breast cancer in different risk groups. Stat Med. 2001 Jan 15;20(1):109-122.
21. Taghipour S. Using Simulation to Model and Validate Invasive Breast Cancer Progression in Women in the Study and Control Groups of the Canadian National Breast Screening Studies I and II. Med Decis Making. 2017 Feb;37(2):212-223.
22. Witteveen A, et al. Risk-based breast cancer follow-up stratified by age. Cancer Med. 2018 Oct;7(10):5291-5298. DOI: 10.1002/cam4.1760
23. Wong SM Breast cancer prevention strategies in lobular carcinoma in situ: A decision analysis. Cancer. 2017 Jul 15;123(14):2609-2617. DOI: 10.1002/cncr.30644
Review
For citations:
Yurkova Yu.P., Kurylev A.A., Kolbin A.S. Overview of mathematical models of breast cancer. Kachestvennaya Klinicheskaya Praktika = Good Clinical Practice. 2019;(2):45-54. (In Russ.) https://doi.org/10.24411/2588-0519-2019-10072
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