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      A contemporary review of breast cancer risk factors and the role of artificial intelligence

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          Abstract

          Background

          Breast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics.

          Methods

          A comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses.

          Results

          Our analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies.

          Conclusion

          The review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.

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          Most cited references113

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            Breast Cancer Statistics, 2022

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              Breast Cancer-Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies-An Updated Review.

              Breast cancer (BC) is the most frequently diagnosed cancer in women worldwide with more than 2 million new cases in 2020. Its incidence and death rates have increased over the last three decades due to the change in risk factor profiles, better cancer registration, and cancer detection. The number of risk factors of BC is significant and includes both the modifiable factors and non-modifiable factors. Currently, about 80% of patients with BC are individuals aged >50. Survival depends on both stage and molecular subtype. Invasive BCs comprise wide spectrum tumors that show a variation concerning their clinical presentation, behavior, and morphology. Based on mRNA gene expression levels, BC can be divided into molecular subtypes (Luminal A, Luminal B, HER2-enriched, and basal-like). The molecular subtypes provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. The eighth edition of TNM classification outlines a new staging system for BC that, in addition to anatomical features, acknowledges biological factors. Treatment of breast cancer is complex and involves a combination of different modalities including surgery, radiotherapy, chemotherapy, hormonal therapy, or biological therapies delivered in diverse sequences.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/1477167Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2710999Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2711178Role: Role: Role:
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                18 April 2024
                2024
                : 14
                : 1356014
                Affiliations
                [1] 1 Engineering Faculty, Universidad Andres Bello , Viña del Mar, Chile
                [2] 2 Centro para la Prevención y Control del Cáncer (CECAN) , Santiago, Chile
                Author notes

                Edited by: Maria Rosaria De Miglio, University of Sassari, Italy

                Reviewed by: Maryam Khayamzadeh, Academy of Medical Sciences, Iran

                Matteo Ghilli, Pisana University Hospital, Italy

                *Correspondence: Orietta Nicolis, orietta.nicolis@ 123456unab.cl
                Article
                10.3389/fonc.2024.1356014
                11063273
                38699635
                ffea67f4-208d-4d77-ac23-ad954ce88a27
                Copyright © 2024 Nicolis, De Los Angeles and Taramasco

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 December 2023
                : 25 March 2024
                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 114, Pages: 18, Words: 5179
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the ANID FONDAP 152220002 (CECAN).
                Categories
                Oncology
                Review
                Custom metadata
                Breast Cancer

                Oncology & Radiotherapy
                breast cancer,risk factors,artificial intelligence (ai),medical history,metabolic factors,reproductive and hormonal factors,lifestyle factors,environmental influence

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