Background: The global health burden of breast cancer is increasing with 5-year survival rates being much shorter in low-income and middle-income countries. Sociodemographic and clinical disparities in early cancer detection affect long-term outcome.
Methods: The authors compared social, demographic, and pathological characteristics associated with metastatic and late stages of breast cancer diagnosis using data collected from a special registry developed by Perhimpunan Bedah Onkologi Indonesia (PERABOI) in 2015.
Results: Of 4959 patients recruited in this study, 995 women (20.1%) were diagnosed with metastatic breast cancer. Lower education status and living in rural areas were significantly associated with Stage IV at diagnosis [odds ratio (OR)=1.256, 95% CI=1.093-1.445, =0.001; and OR=1.197, 95% CI=1.042-1.377, =0.012; respectively). Main complaints other than lump (ulceration, breast pain, and discharge) and occupation as a housewife were also associated with the presentation of metastatic diseases (OR=2.598, 95% CI=2.538-3.448, 0.001 and OR=1.264, 95% CI=1.056-1.567, =0.030, respectively). Having lower education and living outside Java and Bali islands were associated with the diagnosis of late-stage breast cancers (OR=1.908, 95% CI=1.629-2.232, 0.001 and OR=3.039, 95% CI=2.238-4.126, <0.001; respectively). A higher proportion of breast cancer patients were relatively younger with bigger tumour size, positive axillary nodal involvement, and more frequent Human epidermal growth factor receptor 2 overexpression.
Conclusion: The authors identified sociodemographic disparities in the metastatic and late-stage diagnosis of breast cancers among Indonesian women. The subsequent action is required to reduce disparities faced by women with lower social and educational levels for early diagnosis and better healthcare access.
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http://dx.doi.org/10.1097/MS9.0000000000001030 | DOI Listing |
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.
View Article and Find Full Text PDFInt J Gen Med
December 2024
Department of Thyroid and Breast Surgery, Quzhou People's Hospital, Quzhou, 324000, People's Republic of China.
Objective: This study aims to demonstrate the impact of sarcopenia on the prognosis of early breast cancer and its role in early multimodal intervention.
Methods: The clinical data of patients (n=285) subjected to chemotherapy for early-stage breast cancer diagnosed pathologically between January 1, 2016, and December 31, 2020, in our hospital were retrospectively analyzed. Accordingly, the recruited subjects were divided into sarcopenia (n=85) and non-sarcopenia (n=200) groups according to CT diagnosis correlating with single-factor and multifactorial logistic regression analyses.
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