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新方法和建议用于适应风险的乳腺癌筛查。

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening.

机构信息

Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, the Netherlands.

Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands.

出版信息

J Magn Reson Imaging. 2023 Oct;58(4):987-1010. doi: 10.1002/jmri.28731. Epub 2023 Apr 11.

Abstract

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

摘要

基于人群的乳腺癌筛查使用乳腺 X 线摄影作为金标准成像方式已经在临床实践中应用了 40 多年。然而,乳腺 X 线摄影在敏感性和高假阳性率方面的局限性,特别是在高危女性中,挑战了基于人群的筛查的无差别性质。此外,鉴于对新的乳腺癌危险因素的研究不断增加,人们越来越认为乳腺癌筛查应该转向基于风险的方法。乳腺成像技术的最新进展,包括对比增强乳腺 X 线摄影(CEM)、超声(US)(自动乳腺 US、多普勒、弹性成像 US),特别是磁共振成像(MRI)(缩写、超快速、无造影剂),可能为基于风险的个性化筛查策略提供新的机会。此外,人工智能和放射组学技术的整合有可能提高基于风险的筛查性能。本文综述了乳腺癌筛查的当前证据和挑战,并强调了各种成像技术在基于风险的乳腺癌筛查方法中的潜在未来展望。证据水平:1. 技术功效:第 5 阶段。

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