Background: In Morocco, much progress has been made in breast cancer treatment. However, there is limited information on survival outcomes of breast cancer patients according to their therapeutic management.
Methods: A pattern-of-care study was conducted in Morocco's two main oncology centres: Rabat and Casablanca and has shown that major progress has been made in the quality of care with survival rates comparable to those in developed countries. The present study focuses on the different therapeutic strategies used in breast cancer and their impact on prognosis. Patients were classified into two categories: those considered as appropriately managed and those who were not.
Results: A total of 1901 women with stage I to III breast cancer were included in this study, the majority (53%) were adequately managed and had better disease-free survival (DFS) rates than those who were not: DFS at 3 years (88% versus 62%) and at 5 years (80% versus 50%). Potential significant determinants of better management were: treatment in Rabat's oncology centre, treatment between 2008 and 2012, being aged younger than 60 years, and early TN stage.
Conclusion: This study demonstrated the value of proper integrated and coordinated management in a comprehensive cancer centre, to improve breast cancer survival.
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http://dx.doi.org/10.1186/s12885-024-12570-6 | DOI Listing |
Sci Rep
December 2024
Department of Clinical Pharmacy, Baoshan Hospital Affiliated to, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
This study investigates the potential treatment of breast cancer utilizing Gentiana robusta King ex Hook. f. (QJ) through an integrated approach involving network pharmacology, molecular docking, and molecular dynamics simulation.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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