Modeling of the Type of Surgical Intervention for Breast Cancer According to Mammography Examination: Analysis of Factors

Keywords: coefficient of lesion for mammary gland, breast cancer, survival, modeling, analysis of factors, probit regression, statistical significance.


Breast cancer is the most common tumour diagnosis for women worldwide. Over the last 40 years widespread adoption of mammographic screening has established Breast Conserving Surgery (BCS) followed by irradiation as the most practised treatment of choice. However, given the absence of tools to determine the optimal volume of tissue to be excised, the debate continues for achieving a balance between the effectiveness of surgical intervention and the later stage personalization of treatment, and so, a wide variation in practice is a common phenomenon globally.

This study is devoted to modeling and analysis of factors which affect the choice of type and volume of surgical intervention for patients with breast cancer in not at random manner. Given the problems of treating patients with breast cancer, it is extremely important to determine the criteria for an objective choice of the type of surgical intervention at the diagnostic stage. These criteria should ensure both the radical nature of the surgical intervention and the preservation of aesthetically acceptable forms and sizes of the mammary glands.

The study included 73 patients with breast cancer who underwent a mammographic examination and surgery planned according to this examination. The planned type and volume of interventions were compared with the type and volume of the performed ones. Based on the simulation results, the leading mammographic factors were determined.

A statistical model allowing one to quite effectively determine optimal type and volume of surgical intervention based on the data of a mammographic examination and the lesion coefficient as the covariates was built. The proposed model considers the characteristics of the tumor and the anatomical features of patients which, in addition to providing real-time information, enables for predicting the optimal type and amount of surgical intervention. An adequate choice of type of the intervention allows one to plan short-term reconstructive measures in advance, to ensure an adequate quality of life for patients after treatment.


Download data is not yet available.


Prentice, R. L. (1976). A Generalization of the Probit and Logit Methods for Dose Response Curves. Biometrics, Vol. 32, 4, 761–768.

Motuzyuk, I., Sydorchuk, O., Kovtun, N., Palian, Z., & Kostiuchenko, Y. (2018). Analysis of trends and factors in breast multiple primary malignant neoplasms. Breast Cancer: Basic and Clinical Research, Vol. 12, 1–9.

Blair, S. L., Thompson, K., Rococco, J., Malcarne, V., Beitsch, P. D., & Ollila, D. W. (2009). Attaining Negative Margins in Breast-Conservation Operations: Is There a Consensus among Breast Surgeons? Journal of American College of Surgeons, Vol. 209 (5), 608–613.

Lipkus, I. M., Peters, E., & Kimmick, G. (2010). Breast cancer patients’ treatment expectations after exposure to the decision aid program adjuvant online: the influence of numeracy. Medical Decision Making, Vol. 30, 464–473.

Meeting, F. (1996). Breast‐cancer screening with mammography in women aged 40–49 years. International Journal of Cancer, Vol. 68 (6), 693–699.

Duffy, S., Tabar, L., Olsen, A., & Vitak, B. (2010). Cancer mortality in the 50–69 years age group before and after screening. Journal of Medical Screening, Vol. 17 (3), 159–160.

Schwartz, G. F., Veronesi, U., Clough, K. B., Dixon, J. M., Fentiman, I. S., & Heywang-Kobrunner, S. H. et al. (2006). Consensus conference on breast conservation. Journal of the American College of Surgeons, Vol. 203, 198–207.

Coates, P. J., Virginia, M., Appleyard, C. L., Murray, K., Ackland, C., & Gardner, J. et al. (2010). Differential contextual responses of normal human breast epithelium to ionizing radiation in a mouse xenograft model. Cancer Research, Vol. 70, 9808–9815. DOI: 10.1158/0008-5472.CAN-10-1118.

Chen, T. H., Jonsson, S. H. & Lenner, P. (2007). Effect of mammographic service screening on stage at presentation of breast cancers in Sweden. Cancer, Vol. 109 (11), 2205–2212.

Ferlay, J., Shin, H. R., Bray, F. & Forman, D. (2008). Estimates of worldwide burden of cancer in 2008. GLOBOCAN, International Journal of Cancer, Vol. 127, 2893–2917.

Rosenkranz, G. K. (2016). Exploratory subgroup analysis in clinical trials by model selection. Biometrical Journal, Vol. 58 (5), 1007–1259.

Morrow, M., White, J., Moughan, J., Owen, J., Pajack, T., & Sylvester, J. et al. (2001). Factors predicting the use of breast-conserving therapy in stage I and II breast carcinoma. Journal of clinical oncology, Vol. 19 (8), 2254–2262.

Pourhoseingholi, A., Pourhoseingholi, M. A., & Rostami-Nejad, M. (2010). Implementation of statistical analysis in the clinical research of coeliac disease – use of probit and logit analysis. East African Journal of Public Health, Vol. 7, 2, 168–170.

Ekatah, G. E., Turnbull, A. K., Arthur, L. M., Thomas, J., Dodds, C., & Dixon, J. M. (2017). Margin width and local recurrence after breast conserving surgery for ductal carcinoma in situ. European Journal of Surgical Oncology, Vol. 43, 2029–2035.

Dunst, J., & Dellas, K. (2011). Margins! Margins. Margins? How important is margin status in breast-preserving therapy? Breast Care, Vol. 6, 359–362.

Genell, A., Nemes, S., & Steineck, G. (2010). Model selection in medical research: a simulation study comparing Bayesian model averaging and stepwise regression. BMC Medical Research Methodology, Vol. 10, 108.

Thompson, A. M., & Moulder-Thompson, S. L. (2012). Neoadjuvant treatment of breast cancer. Annals of Oncology, 10, 231–236.

Zhu, J. & Xie, J. (2015). Nonparametric Variable Selection for Predictive Models and Subpopulations in Clinical Trials. Journal of Biopharmaceutical Statistics, Vol. 25 (4), 781–794.

Pleijhuis, R.G., Graafland, M., de Vries, J., Bart, J., de Jong, J.S. & van Dam G.M. (2009). Obtaining Adequate Surgical Margins in Breast-Conserving Therapy for Patients with Early-Stage Breast Cancer: Current Modalities and Future Directions. Annals of Surgical Oncology, Vol. 16, 2717–2730.

Jacobs, L. (2008). Positive margins: the challenge continues for breast surgeons. Annals of Surgical Oncology, Vol. 15, 1271–1272.

Pourhoseingholi, A., Pourhoseingholi, M.A. & Vahedi, M. (2008). Relation between demographic factors and type of gastrointestinal cancer using probit and logit regression. Asian Pacific Journal of Cancer Prevention, Vol. 9 (4), 753–755.

Chiappa, C., Rovera, F., Corben, A.D., Fachinetti, A., De Berardinis, V., Marchionini V. & [et al.] (2013). Surgical margins in breast conservation. International Journal of Surgery, Vol. 11 (S1), 69–72.

Morrow, M., Harris, J.R. & Schnitt, J.S. (2012). Surgical margins in lumpectomy for breast cancer – bigger is not better. The New England Journal of Medicine, Vol. 367 (1), 79–82.

Singletary, S.E. (2002). Surgical margins in patients with early-stage breast cancer treated with breast conservation therapy. The American Journal of Surgery, Vol. 184, 383-393.

Tabar, L., Vitak, B., Chen, T.H., Yen, A.M., Cohen, A., & Tot, T. et al. (2011). Swedish two-county trial: impact of mammographic screening on breast cancer mortality during 3 decades. Radiology, Vol. 260 (3), 658–663.

Vicini, F.A., Eberlein, T.J., Connolly, J.L., Recht, A., Abner, A., & Schnitt, S.J. et al. (1991). The optimal extent of resection for patients with stages I or II breast cancer treated with conservative surgery and radiotherapy. Annals of Surgery, Vol. 214, 200–204.

Kovtun, N., Motuzuik, I., Ganzha, R. (2019). A Statistical Modelling approach for guiding the optimum Surgical Intervention of Breast Cancer. Statistics of Ukraine, 2, 42–48.

Abstract views: 72
PDF Downloads: 51
How to Cite
Ganzha, R. O. (2019). Modeling of the Type of Surgical Intervention for Breast Cancer According to Mammography Examination: Analysis of Factors. Statistics of Ukraine, 86(3), 82-89.