Clinical characteristics, risk factors, and outcomes in Chilean triple negative breast cancer patients: a real-world study

Acevedo, Francisco; Walbaum, Benjamín; Medina, Lidia; Merino, Tomás; Camus, Mauricio; Püschel, Klaus; Ramírez, Karol; Manzor, Manuel; Veglia, Paulina; Martínez, Raúl Rúa; Guerra, Constanza; Navarro, Marisel; Bauerle, Catherine; Domı́nguez, Francisco; Sánchez, César

doi:10.1007/s10549-022-06814-x

Cited by 2 publications

(1 citation statement)

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“…Similarly ( 36 ), notes a social gradient in cancer incidence in Costa Rica ( 51 ). links higher education levels to increased breast cancer risk ( 2 ). emphasizes local demographic factors in TNBC (Triple-Negative Breast Cancer) treatment, while ( 32 ) highlights access disparities in Colombia.…”

Section: Resultsmentioning

confidence: 99%

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

Nicolis,

De Los Angeles,

Taramasco

2024

Front. Oncol.

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BackgroundBreast 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.MethodsA 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.ResultsOur 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.ConclusionThe 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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