Browsing by Author "Muñoz, Montserrat"

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    Hormone receptor-positive early breast cancer in young women: A comprehensive review
    (2024) Walbaum García, Benjamín Vicente; García-Fructuoso, Isabel; Martínez-Sáez, Olga; Schettini, Francesco; Sánchez Rojel, César Giovanni; Acevedo Claros, Francisco Nicolás; Chic, Nuria; Muñoz-Carrillo, Javier; Adamo, Barbara; Muñoz, Montserrat; Partridge, Ann H.; Bellet, Meritxell; Brasó-Maristany, Fara; Prat, Aleix; Vidal Losada, Maria
    The incidence of breast cancer in ≤ 40 yr-old women (YWBC) has been steadily increasing in recent decades. Although this group of patients represents less than 10 % of all newly diagnosed BC cases it encompasses a significant burden of disease. Usually underrepresented in clinical trials, YWBCs are also characterized by late diagnoses and poorly differentiated, aggressive-subtype disease, partly explaining its poor prognosis along with a high recurrence risk, and high mortality rates. On the other hand, YWBC treatment poses unique challenges such as preservation of fertility, and long-term toxicity and adverse events. Herein, we summarize the current evidence in hormone receptor-positive YWBC including specific risk factors, clinicopathologic and genomic features, and available evidence on response to chemotherapy and endocrine therapy. Overall, we advocate for a more comprehensive multidisciplinary healthcare model to improve the outcomes and the quality of life of this subset of younger patients.
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    The role of artificial intelligence integrating multi-omics in breast cancer
    (Spanish Society of Senology and Breast Pathology, 2025) Gomez-Bravo, Raquel; Walbaum García, Benjamín Vicente; Segui, Elia; Muñoz, Montserrat
    In an era of precision oncology, genomic testing plays a crucial role in the management of breast cancer. A variety of complex techniques for germline, somatic, and gene expression testing are routinely used as part of our clinical practice. However, challenges remain in both interpreting genomic data and in the ever-expanding breadth of available tumor information. Artificial intelligence (AI), specifically machine learning and deep learning models, can create and facilitate the interpretation of complex genetic data, predict patient outcomes, and personalize treatment plans. Herein, we present a review of the current role of AI integrating multi-omics in BC.