乳腺癌是全球女性癌症相关死亡的主要原因。传统的筛查和风险预测模型主要依靠人口统计学和患者临床病史来制定政策和估计可能性。然而,人工智能(AI)技术的最新进展,特别是深度学习(DL),在开发个性化风险模型方面表现出了希望。这些模型利用从医学成像和相关报告获得的个体患者信息。在这次系统审查中,我们彻底调查了现有的关于DL在数字乳腺X线摄影中应用的文献,影像组学,基因组学,和乳腺癌风险评估的临床信息。我们批判性地分析了这些研究并讨论了他们的发现,强调DL技术预测乳腺癌风险的前景。此外,我们探索了正在进行的研究计划和AI驱动方法的潜在未来应用,以进一步提高乳腺癌风险预测,从而促进更有效的筛选和个性化的风险管理策略。
■本研究全面概述了使用传统和AI模型进行乳腺癌风险预测的成像和非成像特征。这项研究中回顾的特征包括影像学,影像组学,基因组学,和临床特征。此外,这项调查系统地介绍了用于乳腺癌风险预测的DL方法,旨在对初学者和高级研究人员都有用。
■总共确定了600篇文章,其中20个符合设定的标准并被选中。DL模型的并行基准测试,随着自然语言处理(NLP)应用于成像和非成像特征,可以使临床医生和研究人员在考虑临床部署或开发新模型时获得更大的认识。这篇综述为了解使用AI进行乳腺癌风险评估的现状提供了全面的指导。
这项研究为研究人员提供了一个不同的观点,即使用人工智能进行乳腺癌风险预测。结合了许多成像和非成像特征。
UNASSIGNED: Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic
review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies.
UNASSIGNED: This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers.
UNASSIGNED: A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This
review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI.
UNASSIGNED: This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.