Rationale: Globally, breast cancer is by far the most frequently occurring cancer amongst women. Whilst the physical consequences of the disease and associated treatments are well documented, a comprehensive picture of how breast cancer is experienced at all stages of disease progression is lacking.
Objective: This systematic review aimed to synthesize qualitative studies documenting women's breast cancer narratives into an empirically based explanatory framework.
Methods: Two investigators independently searched Academic Search Premiere, CINAHL, Health Source: Nursing/Academic Edition, MEDLINE, PsycARTICLES, PubMed, Science Direct, SCOPUS, Web of Science and three international dissertation repositories using a pre-specified search strategy to identify qualitative studies on women's breast cancer narratives across all geographic and income-level settings. Of the 7840 studies that were screened for eligibility, included in the review were 180 studies, which were assessed using the Critical Appraisal Skills Programme. Using a 'meta-study' approach, an explanatory model of the breast cancer experience was formulated. Finally, we assessed the confidence in the review findings using the 'Confidence in the Evidence from Reviews of Qualitative Research' (CERQual) guidelines.
Results: Eight core themes were identified: the burden of breast cancer, existential ordeal, illness appraisal, sources of support, being in the healthcare system, the self in relation to others, changes in self-image, and survivor identity. Together, these form the proposed Trajectory of Breast Cancer (TBC) framework.
Conclusion: The Trajectory of Breast Cancer explanatory framework offers a theoretically defensible synthesis of women's experiences of breast cancer. This framework provides an empirical basis for future reviewers conducting qualitative and narrative breast cancer research.
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http://dx.doi.org/10.1016/j.socscimed.2019.01.020 | 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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