Full Text

Abstract

Breast Cancer is the second most leading cancer among women in the world. Detecting the disease at the earliest and parallel treatment may significantly increase the survival of the victim. A clinical trial has made an attempt to evaluate the low risk called ductal carcinoma in situ (DCIS), a monitoring approach rather surgery. This paper implements a quantitative model for mining the gene expression of DCIS for early detection of breast cancer under active surveillance that offers a close monitoring for the signs of progression of breast cancer. The proposed work determines whether the low-risk DCIS can undergo active surveillance without degrading the quality of life when compared to the conventional treatments. Our research proposes a classification technique using quantitative model based SVM, a hybrid technique for analyzing the gene expression of low risk patients.

References

1. Erik S. Knudsen, Adam Ertel, Elai Davicioni, Jessica Kline,Gordon F. Schwartz, Agnieszka K. Witkiewicz, Progression of ductal carcinoma in situ to invasive breast cancer is associated with gene expression programs of EMT and myoepithelia, Springer, Volume 133, Issue 3, pp 1009–1024,

2. Mohammed Badruddoja, Ductal Carcinoma In Situ of the Breast: A Surgical Perspective, International Journal of Surgical Oncology, PMC, 2012

3. Uma Ojha, Savita Goel, A study on prediction of breast cancer recurrence using data mining techniques, Cloud Computing, Data science & Engineering –Confluence, 7th International Conference, IEEE, 2017

4. Bryan BB, Schnitt SJ, Collins LC: Ductal carcinoma in situ with basal-like phenotype: a possible precursor to invasive basal-like breast cancer. Mod Pathol. Vol. 19, pp. 617-621, 2006.

5. Livasy CA, Perou CM, Karaca G, Cowan DW, Maia D, Jackson S, Tse CK, Nyante S, Millikan RC: Identification of a basal-like subtype of breast ductal carcinoma in situ. Hum Pathol. Vol. 38, pp. 197-204, 2007.

6. Steinman S, Wang J, Bourne P, Yang Q, Tang P: Expression of cytokeratin markers, ER-alpha, PR, HER-2/neu, and EGFR in pure ductal carcinoma in situ (DCIS) and DCIS with co-existing invasive ductal carcinoma (IDC) of the breast. Ann Clin Lab Sci. Vol. 37, pp. 127-134, 2007.

7. Paredes J, Lopes N, Milanezi F, and Schmitt FC: P-cadherin and cytokeratin 5: useful adjunct markers to distinguish basal-like ductal carcinomas in situ. Virchows Arch. Vol. 450, pp. 73-80, 2007.

8. D. Voth, Using AI to detect breast cancer, IEEE Intelligent Systems, Vol.20 No.1, pp.5-7, 2005.

9. L.J. Esserman, I.M. Thompson, B. Reid, Overdiagnosis and overtreatment in cancer: an opportunity for improvement, JAMA J Am Med Assoc, Vol. 310, pp. 797-798, 2013.

10. L.E. Elshof, K. Tryfonidis, L. Slaets, A.E. van Leeuwen-Stok, V.P. Skinner, N. Dif, e al., Feasibility of a prospective, randomised, open-label, international multicentre, phase III, non-inferiority trial to assess the safety of active surveillance for low risk ductal carcinoma in situ – the LORD study Eur J Cancer, Vol. 51, pp. 1497-1510, 2015.

11. J.S. Wong, Prospective study of wide excision alone for ductal carcinoma in situ of the breast , J Clin Oncol, 24,Vol. 24, pp. 1031-1036, 2006.

12. Emma J. Groen, Lotte E. Elshof, Lindy L. Visser, Emiel J, Th. Rutgers, Hillegonda A.O. Winter-Warnars, Ester H. Lips, Jelle Wesseling, Finding the balance between over- and under- treatment of ductal carcinoma in situ(DCIS), Elsevier, ScienceDirect, Vol.31, pp. 274-283,2017.

13. Anunciacao Orlando, Gomes C. Bruno, Vinga Susana, Gaspar Jorge, Oliveira L. Arlindo and Rueff Jose, “A Data Mining approach for detection of high-risk Breast Cancer groups,” Advances in Soft Computing, vol. 74, pp. 43-51, 2010.

14. Khan M.U., Choi J.P., Shin H. and Kim M, “Predicting Breast Cancer Survivability Using Fuzzy Decision Trees for Personalized Healthcare”, Conf Proc IEEE Eng Med Biol Soc., pp. 48-51, 2008.

15. Abdelaal Ahmed Mohamed Medhat and Farouq Wael Muhamed, “Using data mining for assessing diagnosis of breast cancer,” in Proc. International multiconfrence on computer science and information Technology, pp. 11-17, 2010.

16. Nikunj C. Oza, Ph.D., NASA Ames Research Center, USA, Ensemble Data Mining Methods, https://ntrs.nasa.gov/search.jsp?R=20060015642, 2017.

Keywords

Breast Cancer, Ductal Carcinoma in situ (DCIS), Mining gene expression, Active surveillance, Classification.