Study of histopathological characteristics and hormonal receptors biomarkers of breast cancer in Benghazi medical Centre in the period between (2019-2024)
Background: Breast cancer is considered as the most frequently diagnosed cancer worldwide accounting for 11.7% of new cancer cases in 2020. Thus, breast cancer remains a significant global health concern despite the rapid advances in this field. Material and methods: Study data were collected and medical records of patients at Benghazi Medical Centre were reviewed to obtain data on age, sex, tumour site, nationality, family history, year of diagnosis, site, pathological grade and tumour markers. Result: The results showed that the highest number recorded was 23 cases in 2020 and 22 cases in 2023, the mean age was 53 years, while the age group was from 31 to 80 years, in a geographical area of Benghazi 43%, there were 68 cases from Libya and one case from Egypt, 68% showed a negative family history, and most of the cases were between stage II and stage III. Regarding site of the tumour 49% are in the left side of the tumour site and 47.8% in the right side, and 2.9% were bilateral. Most of the cases were negative for Her2, positive for ER, and positive for PR, regarding expression of Ki-67 level values in 12 cases expression was (20-40%) and in10 cases expression was>80%. Most of the cases 91% the histopathological report reported invasive ductal carcinoma. Conclusion: Early detection and diagnosis using new technologies, improved access to affordable treatment, more palliative care and support for breast cancer research are all important measures that must be addressed to reduce morbidity and mortality.
Authors
- Lobna Abdalla Elfrgani
- Nada Hamad (ORCID: https://orcid.org/0000-0001-7929-1450)
- Naziha Mohamad Bilhassan
- Amina Baleid Mohamed Younis
- Abeer H. Amer
- Ghazala Othman Ali Elfeitouri
- Haneen Ramadan Bozaho
- Farah Miftah Al-Barassi
- Farah Salem Miftah Al-Burki
Publication Details
- Journal
- Open Access Research Journal of Life Sciences
- Published
- 2024-12-18
- DOI
- https://doi.org/10.53022/oarjls.2024.8.2.0037
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00