Comparison of clinical features and the impact of reproductive factors on by age at diagnosis young and elderly breast cancer patients in the middle Anatolian region of Turkey

OBJECTIVE: Reproductive risk factors have been shown to influence breast cancer etiology for women of different origin worldwide: most studies in young/older patients have been limited to analyzing survival or tumor characteristics within their age group. This study aimed to compare the clinicopathological characteristics, treatment regimens. survival outcomes. and the impact of reproductive risk factors on young and elderly breast cancer patients. PATIENTS AND METHODS: The data were collected retrospectively between October 2015 and March 2021. where 77 young patients (<= 40 years) and 107 elderly patients (>65 years) were included out of a total of 567 patients undergoing treatment at Kayseri City Training and Research Hospital General Surgery Clinic, Turkey. Logistic regression analysis was performed to assess the impact of risk factors according to age. RESULTS: Luminal-like tumors were in the majority in both age groups: there was no difference in diagnostic stages and survival between groups. The nulliparity ratio. total breastfeeding duration, number of biological children, first full-term pregnancy age, body mass index (BMI). and breast density distribution were significantly statistically different between groups. According to the multiple binary logistic regression analysis results for age. the most significant factors with risk effects were variables age of menarche OR= 3.36 (95% CI: 1.44-7.86) and child number OR= 2.58 (95% CI: 1.75-3.79), respectively. CONCLUSIONS: The importance of looking at the impact of different risk factors on breast cancer risk lies in the potential to develop valid risk prediction models that can allow targeted screening and preventive interventions for high-risk women. By identifying more influential risk factors in different geographical profiles, risk-based screening, and targeted prevention efforts can be encouraged. and these factors can be included in risk prediction models.