Madani Mohammad, Behzadi Mohammad Mahdi, Nabavi Sheida
Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA.
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.
Cancers (Basel). 2022 Oct 29;14(21):5334. doi: 10.3390/cancers14215334.
Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.
乳腺癌是女性中最常见且致命的疾病之一，目前尚未发现永久性的治疗方法。因此，早期检测是控制和治愈乳腺癌的关键步骤，这可以挽救数百万女性的生命。例如，2020年，超过65%的乳腺癌患者在癌症早期被诊断出来，他们全部存活。尽管早期检测是癌症治疗最有效的方法，但放射科医生进行的乳腺癌筛查非常昂贵且耗时。更重要的是，传统的乳腺癌图像分析方法存在较高的误诊率。不同的乳腺癌成像模态被用于提取和分析影响乳腺癌诊断和治疗的关键特征。这些成像模态可分为多个子类别，如乳房X光片、超声、磁共振成像、组织病理学图像，或它们的任何组合。放射科医生或病理学家手动分析这些方法产生的图像，这增加了癌症检测错误决策的风险。因此，需要利用新的自动方法来分析各种乳腺筛查图像，以协助放射科医生解读图像。最近，人工智能（AI）已被广泛用于自动改善不同类型癌症，特别是乳腺癌的早期检测和治疗，从而提高患者的生存几率。人工智能算法的进步，如深度学习，以及从各种成像模态获得的数据集的可用性，为突破当前乳腺癌分析方法的局限性提供了机会。在本文中，我们首先回顾乳腺癌成像模态及其优缺点。然后，我们探索并总结了最近使用人工智能通过各种乳腺成像模态进行乳腺癌检测的研究。此外，我们报告了有关乳腺癌成像模态的可用数据集，这些数据集对于开发基于人工智能的算法和训练深度学习模型很重要。总之，这篇综述文章试图提供一个全面的资源，以帮助从事乳腺癌成像分析的研究人员。
