Author:
Nicolis Orietta,De Los Angeles Denisse,Taramasco Carla
Abstract
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.
Reference114 articles.
1. Breast cancer
2. Clinical characteristics, risk factors, and outcomes in Chilean triple negative breast cancer patients: a real-world study;Acevedo;Breast Cancer Res Treat,2023
3. The molecular and genetic interactions between obesity and breast cancer risk;Ajabnoor;Medicina,2023
4. Race, ethnicity, and clinical outcomes in hormone receptor-positive, her2-negative, node-negative breast cancer in the randomized tailorx trial;Albain;JNCI: J Natl Cancer Institute,2021
5. Predicting breast cancer from risk factors using svm and extra-trees-based feature selection method;Alfian;Computers,2022
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献