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Purpose: To evaluate the role of diffusion-weighted imaging (DWI) in the detection of breast cancers, and to correlate the apparent diffusion coefficient (ADC) value with prognostic factors.
Materials And Methods: Sixty-seven women with invasive cancer underwent breast MRI. Histological specimens were analyzed for tumor size and grade, and expression of estrogen receptors (ER), progesterone receptors, c-erbB-2, p53, Ki-67, and epidermal growth factor receptors. The computed mean ADC values of breast cancer and normal breast parenchyma were compared. Relationships between the ADC values and prognostic factors were determined using Wilcoxon signed rank test and Kruskal-Wallis test.
Results: DWI detected breast cancer as a hyperintense area in 62 patients (92.5 %). A statistically significant difference in the mean ADC values of breast cancer (1.09 +/- 0.27 x 10(-5) mm(2)/s) and normal parenchyma (1.59 +/- 0.27 x 10(-5) mm(2)/s) was detected (P < 0.0001). There were no correlations between the ADC value and prognostic factors. However, the median ADC value was lower in the ER-positive group than the ER negative group, and this difference was marginally significant (1.09 x 10(-5) mm(2)/s versus 1.15 x 10(-5) mm(2)/s, P = 0.053).
Conclusion: The ADC value was a helpful parameter in detecting malignant breast tumors, but ADC value could not predict patient prognosis.
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http://dx.doi.org/10.1002/jmri.21884 | 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.