Background: In Mexico, breast cancer is the leading cause of death by malignant tumors in women aged 20 and older. The World Health Organization estimates that 69% of deaths caused by breast cancer occur in developing countries. Little is known about the prevalence of breast carcinoma in Mexico and its molecular subclassification.
Methods: This retrospective cross-sectional study included patients who underwent a mastectomy (single, radical or lumpectomy) or a breast tumor biopsy (core-needle or excisional) from January 2002 to December 2018. The primary purpose of the study was to determine the prevalence and molecular profile of breast in comprehensive cancer center in Mexico and compare our results with those published in the US. This study was approved by our scientific and bioethical committee.
Results: The final analysis included 379 patients. The youngest patient was 23 years old and the oldest patient was 89; the mean age at diagnosis was 54.63 years. Patients of 40 years old or younger accounted for 48 of the cases (12.66%) and those older than 40 accounted for 331 of the cases (87.33%). The molecular subclassification showed luminal A subtype in 139 cases (36.67%), luminal B subtype in 143 cases (37.73%), human epidermal growth factor receptor 2-positive carcinomas in 32 cases (8.44%) and triple-negative carcinomas in 65 cases (17.15%). Diabetes mellitus was present in 43 patients (11.34%), hypertension in 78 patients (20.58%), obesity in 82 patients (21.63%) and 66 patients reported being treated with exogenous hormone therapy (17.41%).
Conclusions: Breast carcinoma occurs at an earlier age in Mexican women compared to women in the US. Hormone-positive tumors were found to be more prevalent in older patients, while high-grade tumors were more frequently identified in younger patients.
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http://dx.doi.org/10.14740/wjon1392 | DOI Listing |
Sci Rep
December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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View Article and Find Full Text PDFthe evolution of axillary management in breast cancer has witnessed significant changes in recent decades, leading to an overall reduction in surgical interventions. There have been notable shifts in practice, aiming to minimize morbidity while maintaining oncologic outcomes and accurate staging for newly diagnosed breast cancer patients. These advancements have been facilitated by the improved efficacy of adjuvant therapies.
View Article and Find Full Text PDFthe axillary reverse mapping (ARM) procedure aims to preserve the lymphatic drainage structures of the upper extremity during axillary surgery for breast cancer, thereby reducing the risk of lymphedema in the upper limb. Material and this prospective study included 57 patients with breast cancer who underwent SLNB and ARM. The sentinel lymph node (SLN) was identified using a radioactive tracer.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.
The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.
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