Background: The internal mammary artery and vein is often used as a site of anastomoses in microvascular breast reconstruction. This area supports lymphatic drainage of the breast and its role in breast cancer metastasis remains unclear. We hypothesize that sampling of internal mammary lymph nodes at the time of microvascular anastomoses preparation may identify persistent or recurrent local disease and mandate the need for additional treatment in this area.
Material/methods: A retrospective chart review from 519 patients in the time between January 2006 and September 2009 was performed on all patients who underwent internal mammary lymph node sampling at the time of microvascular breast reconstruction.
Results: Microvascular breast reconstruction was performed in 519 patients. Enlarged internal mammary lymph nodes were found and harvested in 195 patients for histological review. Six of 195 (3.08%) were found positive for metastatic disease requiring additional oncologic treatment.
Conclusions: The internal mammary lymphatic drainage system is an important and often underappreciated pathway for breast metastasis. Routine sampling of these lymph nodes at the time of microvascular breast reconstruction is easy to perform and is a useful tool to identify women, who might require additional treatment and increase cancer-free survival.
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http://dx.doi.org/10.12659/msm.883486 | DOI Listing |
Angiology
January 2025
Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX, USA.
Breast cancer is the most common malignancy among women. While advances in detection and treatment have improved survival, breast cancer survivors face an increased risk of cardiovascular disease. However, limited data exist on cardiac outcomes after ST-elevation myocardial infarction (STEMI) in this population.
View Article and Find Full Text PDFFront Oncol
January 2025
Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Breast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study's systematic review and meta-analysis.
Methods: Three online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms ("Breast Cancer", "Survival Prediction", and "Machine Learning") and their synonyms.
J Clin Nurs
January 2025
McGrath Foundation, North Sydney, New South Wales, Australia.
Aim: To develop and psychometrically test two newly developed Cancer Nurse Self-Assessment Tools for early and metastatic breast cancer (CaN-SAT-eBC and CAN-SAT-mBC).
Design: Instrument development and psychometric testing of content validity, reliability and construct validity.
Methods: A three-phase procedure was conducted.
Mol Cancer
January 2025
Department of Gynecology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, People's Republic of China.
Cancer remains a formidable global health challenge, necessitating innovative therapeutic approaches to enhance treatment efficacy and reduce adverse effects. The traditional Chinese medicine (TCM), as an embodiment of ancient wisdom, has been validated to regulate the holistic human capacity against both internal and external "evils" in accordance with TCM principles. Therefore, it stands to reason to integrate TCM into current cancer therapy paradigms, such as chemotherapy, immunotherapy, and targeted therapy.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, R3B 2E9, Canada.
Background: Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take information from protein sequences into consideration. Deep learning can automatically learn features from protein sequences and protein-protein interactions for hierarchical clustering.
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