Suppr超能文献

超越乳腺密度:多种影像学模式下乳腺癌的风险评估指标。

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.

机构信息

From the Department of Radiology, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L., W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R., L.M.).

出版信息

Radiology. 2023 Mar;306(3):e222575. doi: 10.1148/radiol.222575. Epub 2023 Feb 7.

Abstract

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.

摘要

乳腺密度是乳腺癌的一个独立危险因素。在数字乳腺 X 线摄影和数字乳腺断层合成摄影中,乳腺密度使用美国放射学院乳腺成像报告和数据系统(截至 2022 年 11 月的第 5 版)开发的四分类量表进行视觉评估。基于流行病学的风险模型,如 Tyrer-Cuzick 模型(第 8 版),在纳入乳腺密度后表现出更好的建模性能。除了密度之外,乳腺的另一个独立的乳腺癌风险测量指标是实质组织纹理复杂性。随着放射组学和深度学习的进步,可以定量评估乳腺的纹理模式,并将其纳入风险模型。其他补充的筛查方式,如乳腺超声和 MRI,提供了与乳腺 X 线摄影衍生风险指标互补的独立风险指标。乳腺超声可以将纤维腺体组织的两个成分(间质和腺体)以乳腺 X 线摄影无法做到的方式分开显示。在筛查性乳腺超声中,腺体成分较高与风险较高相关。在 MRI 中,纤维腺体组织的背景实质增强也已成为风险评估的影像学标志物。在乳腺 X 线摄影、超声和 MRI 中观察到的影像学标志物是除了乳腺密度之外,精细预测乳腺癌风险的有力工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8de3/9968778/f8660cb36f3f/radiol.222575.VA.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验