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Objective In 2020, breast cancer was the most commonly diagnosed cancer among women in Japan. Its incidence begins to rise in the late twenties and reaches a first peak in the late forties. Therefore, fostering sustainable preventive health behaviors from a younger age is crucial. This study aims to evaluate the effectiveness of an educational awareness program on breast cancer from a long-term perspective by comparing knowledge levels before the intervention, immediately after, and one month following the intervention, with the goal of promoting breast awareness. Methods and materials This was a cross-sectional study conducted at three time points: before the breast cancer educational intervention, immediately after, and one and a half months post-intervention. A self-administered questionnaire was distributed to 82 first-year students enrolled in the Nursing Course at the School of Health Sciences, Hirosaki University, Japan. Data from 55 students (valid response rate: 64.7%) who completed all three surveys were analyzed. The number of respondents at each time point was: 67 students before the intervention (response rate: 81.7%), 72 students immediately after (response rate: 87.8%), and 63 students one and a half months later (response rate: 76.8%). Ultimately, data from the 55 students with no missing values across all three surveys were included in the analysis. The questionnaire covered topics such as breast cancer subtypes, hereditary breast and ovarian cancer (HBOC), cancer staging, peak age of incidence starting at 40, and invasive vs. non-invasive cancer. Results In the question "Which of the following is considered early-stage cancer?", no significant difference was observed among students before the intervention, on the day of the intervention, or immediately after the intervention. Regarding the question "At what age range does the first peak in the incidence of breast cancer occur in women?", scores significantly decreased immediately after the intervention and one and a half months later (p<0.001). Due to the low scores obtained, the present results suggest that a single awareness education session was not sufficient for a thorough understanding of the peak age for the development of breast cancer. Conclusion While the educational awareness program that provided knowledge as a preliminary step towards breast awareness showed some effectiveness immediately after the intervention, the present results indicate that a single session was insufficient for sustaining knowledge over time. Future educational efforts need to emphasize the peak incidence age of 40 years and incorporate repeated sessions in order to enhance the long-term retention of knowledge.
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http://dx.doi.org/10.7759/cureus.89378 | DOI Listing |
JCO Glob Oncol
May 2025
Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, India.
Purpose: Breast cancer remains a significant public health challenge globally, as well as in India, where it is the most frequently diagnosed cancer in females. Significant disparities in incidence, mortality, and access to health care across India's sociodemographically diverse population highlight the need for increased awareness, policy reform, and research.
Design: This review consolidates data from national cancer registries, global cancer databases, and institutional findings from a tertiary care center to examine the epidemiology, clinical challenges, and management gaps specific to India.
J Med Screen
September 2025
The Cancer Registry of Norway, Department of Screening programs, Norwegian Institute of Public Health, Oslo, Norway.
ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.
View Article and Find Full Text PDFJ Natl Cancer Inst
September 2025
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States.
Background: Among childhood cancer survivors, germline rare variants in autosomal dominant cancer susceptibility genes (AD CSGs) could increase subsequent neoplasm (SNs) risks, but risks for rarer SNs and by age at onset are not well understood.
Methods: We pooled the Childhood Cancer Survivor Study and St Jude Lifetime Cohort (median follow-up = 29.7 years, range 7.
PLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.
Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.