Objective: To investigate the clinical characteristics and prognosis of patients with different subtypes of breast cancer: basaloid, HER-2 and luminal types, and try to find the evidence of individualized treatment for the patients.
Methods: 1280 histologically and immunohistochemically proven patients with resectable breast cancer were treated, and the clinical data including characteristics, relapse and survival of the patients with different subtypes of breast cancer were analyzed retrospectively.
Results: Of the 1280 breast cancer patients, basaloid, HER-2 and luminal types accounted for 20.9%, 23.2% and 55.9%, respectively. Basaloid type was more likely to be found in younger patients frequently with a family history of breast cancer. HER-2 type usually had a tumor of larger size with more advanced stage disease and more metastatic lymph nodes. Luminal type was likely to occur in aged patients with an earlier stage disease. The recurrence rates in basaloid, HER-2 and luminal types were 25.0%, 27.9% and 11.7%, respectively. Patients with basaloid or HER-2 type were found to have a significantly higher recurrence rate than the patients with luminal type breast cancer (P < 0.001), but no significant difference was observed between the basaloid and HER-2 types. However, patients with basaloid type breast cancer were more likely to develop lung metastasis than HER-2 type (13.4% vs. 7.1%, P = 0.017). Up to December 2006, the 5-year disease-free survival (DFS) rates for patients with basaloid, HER-2 and luminal types were 72.2%, 68.2% and 86.2% (P < 0.001), respectively. The overall 5-yr survival (OS) rates of the three groups were 88.6%, 83.8% and 95.8% (P < 0.001) , respectively. Of the patients with luminal type breast cancer, HER2-negative patients had a higher DFS (86.2% vs 57.0%, P < 0.001) and OS (95.8% vs 87.7%, P = 0.0001) compared with those with HER2-positive. The results of Multivariate Cox Regression showed that tumor size and lymph node state were the most important factors influencing the prognosis.
Conclusion: Each subtype of breast cancer has somewhat its own specific clinical features in terms of recurrence pattern and prognosis, therefore, individualized treatment regimen may be required.
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East Mediterr Health J
December 2024
Department of Radiology, King Abdulaziz University, Jeddah, Saudi Arabia.
Background: Breast cancer is often thought to occur at a younger age among Arab women based on the mean or median age at diagnosis, or the proportion of women diagnosed with breast cancer at a young age.
Objective: To compare age-specific breast cancer incidence rates among women from selected Arab countries with selected high- and middle-income countries.
Methods: We examined population-based, age-specific, national or regional breast cancer incidence data for 2008-2012 and 2013-2017 from Australia, Brazil, Canada, Germany, Japan, United Kingdom, and United States of America, and compared them with data from Algeria, Bahrain, Jordan, Kuwait, Morocco, Qatar, and Saudi Arabia.
Pharm Dev Technol
January 2025
Department of Pharmacy, School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, China.
In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.
View Article and Find Full Text PDFJ Med Econ
January 2025
UNESCO-TWAS, The World Academy of Sciences, Trieste, Italy.
Aim: Dynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research quality.
Methods: Articles were searched in PubMed, Web of Science, and Scopus from the inception of the study to November 15th, 2023.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
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