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Ethnicity-Related Survival Analysis of Patients with Triple-Negative Breast Cancer

11 pagesPublished: March 13, 2019


Breast cancer prognostication is a vital element for providing effective treatment for breast cancer patients. Different types of breast cancer can be identified based on the existence or lack of certain receptors (i.e., estrogen, progesterone, her2 receptors). Triple-negative breast cancer (TNBC) is characterized by a lack of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression. Existing studies suggest that TNBC patients tend to have worse prognosis compared to non-TNBC counterparts. The incidence of breast cancer and prognosis in women differ according to ethnicity. Given the poor prognosis of TNBC, cancer-related outcomes must be estimated accurately. Several factors responsible for the poor clinical outcomes observed in TNBC, including age, race/ethnicity, grade, tumor size, lymph node status among others, have been studied extensively. Available research data are not conclusive enough to make a convincing argument for or against a biological or clinical difference in TNBC patients based on these factors. This study was designed to investigate the effects of the ethnicity on breast cancer survivability among TNBC patients utilizing population-based Surveillance, Epidemiology, and End Results (SEER) data to confirm whether ethnicity factor has prognostic significance.

Keyphrases: breast cancer, ethnicity, prognosis, survival analysis, triple negative breast cancer

In: Gordon Lee and Ying Jin (editors). Proceedings of 34th International Conference on Computers and Their Applications, vol 58, pages 236--246

BibTeX entry
  author    = {Mohammad Owrang Ojaboni and Yasmine Kanaan and Robert Dewitty Jr},
  title     = {Ethnicity-Related Survival Analysis of Patients with Triple-Negative Breast Cancer},
  booktitle = {Proceedings of 34th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {58},
  pages     = {236--246},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/f7fq}}
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