Association between epidemiological and clinico-pathological features of breast cancer with prognosis, family history, Ki-67 proliferation index and survival in Tunisian breast cancer patients.

Abstract

Breast cancer has different epidemio-clinical characteristics in Middle East and North-African populations compared to those reported in the Western countries. The aim of this study is to analyze the epidemiological and clinico-pathological features of breast cancer in Tunisia and to determine prognostic factors with special interest to family history, Ki-67 proliferation index and comorbidity. We retrospectively reviewed epidemiological and clinico-pathological data from patients' medical records, treated in the Medical Oncology Department at Abderrahmane Mami Hospital, in the period 2011-2015. Data has been collected on 602 breast cancer patients and analyzed using SPSS software V.23.0. Our study showed high fractions of young breast cancer patients and cases with dense breasts. The most prevalent comorbidities observed in the studied cohort were cardiovascular diseases and diabetes. Familial breast cancer was found in 23.3% of cases and was associated with younger age at diagnosis (p<0.001) and advanced stage (p = 0.015). Ki-67 index >20% was significantly associated with early age at diagnosis, lymph node involvement (p = 0.002), advanced tumor grade (p<0.001) and high risk of relapse (p = 0.007). Ki-67 cut-off 30% predicted survival in luminal cases. Survival was worse in patients with triple negative breast cancer compared to non-triple negative breast cancer, inflammatory breast cancer compared to non-inflammatory breast cancer, moderately to poorly differentiated tumors compared to well-differentiated tumors and with positive lymph nodes compared to pN0 (p<0.05). Our study showed new insights into epidemiological and clinico-pathological characteristics of breast cancer that are not well explored in Tunisian population. Considering our findings along with the implementation of electronic health record system may improve patient health care quality and disease management.