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Understanding metastasis mixed-treatment responses through genomic analyses.
NPJ Breast Cancer
January 2025
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Early-stage and metastatic breast cancers (MBC) can exhibit genomic heterogeneity, even within the same individual. Response to therapy in metastatic breast cancer patients with multiple metastases can also be heterogeneous, with different degrees of responsiveness to the same drug(s) across metastatic sites, termed "mixed response," within the same patient. Whether this treatment response variability is influenced by factors such as intrinsic tumor characteristics of metastatic lesions and/or the microenvironment is unknown.
View Article and Find Full Text PDFFAP-targeted PET/CT imaging in patients with breast cancer from a prospective bi-center study: insights into diagnosis and clinic management.
Eur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine and Minnan PET Center, Xiamen Key Laboratory of Radiopharmaceuticals, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Purpose: To evaluate the diagnostic accuracy and clinical impact of fibroblast activation protein (FAP)-targeted PET/CT imaging in primary and metastatic breast cancer and compare the results with those of standard-of-care imaging (SCI) and [F]FDG PET/CT.
Methods: We prospectively analyzed patients with diagnosed or suspected breast cancer who underwent concomitant FAP-targeted PET/CT (radiotracers including either [Ga]Ga-FAPI-46 or [F]FAPI-42) and [F]FDG PET/CT scans from June 2020 to January 2024 at two medical centers. Breast ultrasound (US) imaging was performed in all treatment-naïve patients as SCI.
CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology.
Breast Cancer (Auckl)
January 2025
Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
Background: Texture analysis has the potential to deliver quantitative imaging markers. Patients receiving computed tomography (CT)-guided percutaneous bone biopsies could be characterized using texture analysis derived from CT. Especially for breast cancer (BC) patients, it could be crucial to better predict the outcome of the biopsy to better reflect the immunohistochemistry status of the tumor.
View Article and Find Full Text PDFEvaluation of the role of circular RNA (circ-FAF1) as a diagnostic biomarker for breast cancer in a cohort of Egyptian breast cancer patients.
Mol Biol Rep
January 2025
Department of Clinical Pathology, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Background: The identification of circulating potential biomarkers may help earlier diagnosis of breast cancer, which is critical for effective treatment and better disease outcomes. We aimed to study the role of circ-FAF1 as a diagnostic biomarker in female breast cancer using peripheral blood samples of these patients, and to investigate the relation between circ-FAF1 and different clinicopathological features of the included patients.
Methods And Results: This case-control study enrolled 60 female breast cancer patients and 60 age-matched healthy control subjects.
Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.
Sci Rep
January 2025
Center for Informatics Science (CIS), School of Information Technology and Computer Science, Nile University, 26th of July Corridor, Sheikh Zayed City, Giza, 12588, Egypt.
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns.
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