Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China.
Phys Med Biol. 2023 Nov 28;68(23). doi: 10.1088/1361-6560/acfade.
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
乳腺癌是人类最常见的恶性肿瘤类型,也是女性死亡的主要原因之一。针对个体患者,采用了标准的治疗策略,包括新辅助化疗、手术、术后化疗、靶向治疗、内分泌治疗和放疗等。这些个性化治疗方案极大地降低了女性罹患乳腺癌的威胁。此外,早期影像学筛查在减少治疗周期和改善乳腺癌预后方面发挥着重要作用。近年来,人工智能(AI)的创新革命为放射科医生在乳腺癌的早期和准确诊断提供了帮助。在这篇综述中,我们介绍了将 AI 纳入乳腺成像的必要性,以及基于 1994 年以来发表的文章,AI 在乳腺 X 线摄影、超声、磁共振成像和正电子发射断层扫描/计算机断层扫描中的应用。此外,还讨论了 AI 在乳腺成像中的挑战。