ABSTRACT
Introduction Breast cancer remains the most significant cancer affecting women worldwide, with an increasing incidence, especially in developing regions. The introduction of genomic tests like Oncotype DX has revolutionized personalized treatment, allowing for more tailored approaches to therapy. This study focuses on the United Arab Emirates (UAE), where breast cancer is the leading cause of cancer-related deaths among women, aiming to assess the predictive accuracy of the Oncotype DX test in categorizing patients based on recurrence risk. Materials and methods A retrospective cohort study was conducted on 95 breast cancer patients diagnosed at Tawam Hospital between 2013 and 2017 who underwent Oncotype DX testing. Data on patient demographics, tumor characteristics, treatment details, and Oncotype DX scores were collected. Survival analysis was performed using the Kaplan-Meier method, with the chi-square goodness of fit test assessing the model's adequacy. Results The cohort's age range was 27-71 years, with a mean age of 50, indicating a significant concentration of cases in the early post-menopausal period. The Oncotype DX analysis classified 55 patients (57.9%) as low risk, 29 (30.5%) as medium risk, and 11 (11.6%) as high risk of recurrence. The majority, 73 patients (76.8%), did not receive chemotherapy, highlighting the test's impact on treatment decisions. The survival analysis revealed no statistically significant difference in recurrence rates across the Oncotype DX risk categories (p = 0.268231). Conclusion The Oncotype DX test provides a valuable genomic approach to categorizing breast cancer patients by recurrence risk in the UAE. While the test influences treatment decisions, particularly the use of chemotherapy, this study did not find a significant correlation between Oncotype DX risk categories and actual recurrence events. These findings underscore the need for further research to optimize the use of genomic testing in the UAE's diverse patient population and enhance personalized treatment strategies in breast cancer management.
ABSTRACT
Introduction This study delves into the complex interplay between diabetes and breast cancer within the United Arab Emirates (UAE), a subject of considerable global health concern. Given the increasing incidence of both diseases worldwide, this research investigates explicitly the potential influence of diabetes on the staging of breast cancer. The UAE, mirroring global trends, has experienced a surge in both conditions attributed to a blend of genetic, environmental, and lifestyle factors. The core objective of this investigation is to explore the link between diabetes and the stage at which breast cancer is diagnosed in UAE patients. Material and method To conduct this study, data were extracted from an extensive medical database consisting of anonymized records about breast cancer patients and their comorbid conditions. The research encompassed adult patients of all genders, all of whom had been definitively diagnosed with breast cancer. The data was analyzed using a suite of Python libraries, including Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, and Seaborn. Descriptive and inferential statistical methods were employed, focusing on the Chi-Square test and logistic regression analysis to evaluate the relationship between diabetes and the stages of breast cancer, considering other comorbidities as well. Results The analysis included 131 breast cancer patients, predominantly female (98.47%), with an average age of 54.2 years. Among these patients, 22.14% were diabetic. The prevalence of other comorbidities, such as dyslipidemia, hypertension, and hypothyroidism, was also recorded. The Chi-Square test indicated no significant correlation between diabetes and the stages of breast cancer (χ² = 3.07, p = 0.381). Stage II was the most frequently diagnosed, irrespective of the presence or absence of diabetes. Conclusion In conclusion, this study finds no substantial link between diabetes and the stage of breast cancer diagnosis among patients in the UAE after adjusting for age and other comorbid conditions. These results underscore the need for early breast cancer detection approaches that are not exclusively dependent on the diabetic status of the patients. However, limitations such as the retrospective cohort design and the relatively small sample size highlight the necessity for further comprehensive studies. Such research would deepen the understanding of the relationship between diabetes and breast cancer and contribute to the advancement of breast cancer healthcare in the UAE.
