Hussain Sadam, Ali Mansoor, Naseem Usman, Nezhadmoghadam Fahimeh, Jatoi Munsif Ali, Gulliver T Aaron, Tamez-Peña Jose Gerardo
School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico.
Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada.
Front Oncol. 2024 Mar 20;14:1343627. doi: 10.3389/fonc.2024.1343627. eCollection 2024.
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.
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.
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.
This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.
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，特别是深度学习（DL）的最新进展，在个性化风险模型的开发中显示出了前景。这些模型利用从医学影像和相关报告中获得的个体患者信息。在本系统综述中，我们全面调查了关于将DL应用于数字乳腺摄影、放射组学、基因组学和临床信息以进行乳腺癌风险评估的现有文献。我们对这些研究进行了批判性分析并讨论了其结果，突出了DL技术在乳腺癌风险预测方面的广阔前景。此外，我们探索了正在进行的研究计划以及人工智能驱动方法的潜在未来应用，以进一步改善乳腺癌风险预测，从而促进更有效的筛查和个性化风险管理策略。
本研究全面概述了使用传统模型和人工智能模型进行乳腺癌风险预测时所使用的影像和非影像特征。本研究综述的特征包括影像、放射组学、基因组学和临床特征。此外，本调查系统地介绍了为乳腺癌风险预测开发的深度学习方法，旨在对初学者和高级研究人员都有用。
共识别出600篇文章，其中20篇符合设定标准并被选中。DL模型的并行基准测试，以及应用于影像和非影像特征的自然语言处理（NLP），可以让临床医生和研究人员在考虑新模型的临床部署或开发时获得更多认识。本综述为理解使用人工智能进行乳腺癌风险评估的现状提供了全面指南。
本研究为研究人员提供了关于使用人工智能进行乳腺癌风险预测的不同视角，纳入了众多影像和非影像特征。
