• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

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.

DOI:10.1148/radiol.222575
PMID:36749212
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9968778/
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
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8de3/9968778/f8660cb36f3f/radiol.222575.VA.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8de3/9968778/f8660cb36f3f/radiol.222575.VA.jpg

相似文献

1
Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.超越乳腺密度:多种影像学模式下乳腺癌的风险评估指标。
Radiology. 2023 Mar;306(3):e222575. doi: 10.1148/radiol.222575. Epub 2023 Feb 7.
2
Long-term Accuracy of Breast Cancer Risk Assessment Combining Classic Risk Factors and Breast Density.经典风险因素与乳腺密度相结合的乳腺癌风险评估的长期准确性。
JAMA Oncol. 2018 Sep 1;4(9):e180174. doi: 10.1001/jamaoncol.2018.0174. Epub 2018 Sep 13.
3
Quantitative breast density analysis using tomosynthesis and comparison with MRI and digital mammography.基于断层合成术的乳腺密度定量分析,并与 MRI 和数字乳腺 X 线摄影比较。
Comput Methods Programs Biomed. 2018 Feb;154:99-107. doi: 10.1016/j.cmpb.2017.11.008.
4
Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment.乳腺实质复杂性的放射组学表型:在乳腺癌风险评估中增强乳腺密度。
Radiology. 2019 Jan;290(1):41-49. doi: 10.1148/radiol.2018180179. Epub 2018 Oct 30.
5
Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study.乳腺X线密度和结构特征在乳腺X线筛查中可单独或共同影响乳腺癌风险评估:一项病例对照研究。
BMC Cancer. 2016 Jul 7;16:414. doi: 10.1186/s12885-016-2450-7.
6
Mammographic density, MRI background parenchymal enhancement and breast cancer risk.乳腺密度、MRI 背景实质增强与乳腺癌风险。
Ann Oncol. 2013 Nov;24 Suppl 8(Suppl 8):viii37-viii41. doi: 10.1093/annonc/mdt310.
7
Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer.乳腺筛查评估病例和有乳腺癌家族史的女性的乳腺密度与乳腺癌风险。
Eur J Cancer. 2018 Jan;88:48-56. doi: 10.1016/j.ejca.2017.10.022. Epub 2017 Nov 27.
8
Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions.乳腺钼靶密度:当前评估方法、临床意义及未来方向。
Semin Ultrasound CT MR. 2023 Feb;44(1):35-45. doi: 10.1053/j.sult.2022.11.001. Epub 2022 Nov 4.
9
A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort.利用亚洲队列的乳腺 X 线图像实质纹理分析来确定乳腺癌风险的新方法。
Phys Med Biol. 2019 Jan 31;64(3):035016. doi: 10.1088/1361-6560/aafabd.
10
Association between Breast Parenchymal Complexity and False-Positive Recall From Digital Mammography Versus Breast Tomosynthesis: Preliminary Investigation in the ACRIN PA 4006 Trial.乳腺实质复杂性与数字乳腺摄影和乳腺断层合成假阳性召回之间的关联:ACRIN PA 4006试验的初步调查
Acad Radiol. 2016 Aug;23(8):977-86. doi: 10.1016/j.acra.2016.02.019. Epub 2016 May 25.

引用本文的文献

1
Exploring the use of large language models for classification, clinical interpretation, and treatment recommendation in breast tumor patient records.探索大语言模型在乳腺肿瘤患者记录的分类、临床解读及治疗推荐中的应用。
Sci Rep. 2025 Aug 26;15(1):31450. doi: 10.1038/s41598-025-16999-y.
2
Deep Learning-Based Recurrence Prediction in HER2-Low Breast Cancer: Comparison of MRI-Alone, Clinicopathologic-Alone, and Combined Models.基于深度学习的HER2低表达乳腺癌复发预测:单纯MRI、单纯临床病理及联合模型的比较
Diagnostics (Basel). 2025 Jul 29;15(15):1895. doi: 10.3390/diagnostics15151895.
3
Stratifying Breast Lesion Risk Using BI-RADS: A Correlative Study of Imaging and Histopathology.

本文引用的文献

1
Background Parenchymal Enhancement at Postoperative Surveillance Breast MRI: Association with Future Second Breast Cancer Risk.背景:术后乳腺 MRI 随访中实质强化与未来二次乳腺癌风险的相关性。
Radiology. 2023 Jan;306(1):90-99. doi: 10.1148/radiol.220440. Epub 2022 Aug 30.
2
A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care.一种用于数字乳腺断层合成的风险模型,以预测乳腺癌并指导临床护理。
Sci Transl Med. 2022 May 11;14(644):eabn3971. doi: 10.1126/scitranslmed.abn3971.
3
Risk Assessment in Population-Based Breast Cancer Screening.
使用乳腺影像报告和数据系统(BI-RADS)对乳腺病变风险进行分层:影像学与组织病理学的相关性研究
Medicina (Kaunas). 2025 Jul 10;61(7):1245. doi: 10.3390/medicina61071245.
4
Comparison of Digital Breast Tomosynthesis and Mammography-based Radiomics for Breast Cancer Risk Assessment: Case-Control Study.数字化乳腺断层合成与基于乳腺X线摄影的影像组学在乳腺癌风险评估中的比较:病例对照研究
Radiol Imaging Cancer. 2025 Jul;7(4):e240318. doi: 10.1148/rycan.240318.
5
Robust evaluation of tissue-specific radiomic features for classifying breast tissue density grades.用于乳腺组织密度分级的组织特异性放射组学特征的稳健评估
J Med Imaging (Bellingham). 2025 Nov;12(Suppl 2):S22010. doi: 10.1117/1.JMI.12.S2.S22010. Epub 2025 May 29.
6
Exploring the Evolution of Breast Cancer Imaging: A Review of Conventional and Emerging Modalities.探索乳腺癌成像的演变:传统与新兴模式综述
Cureus. 2025 Apr 22;17(4):e82762. doi: 10.7759/cureus.82762. eCollection 2025 Apr.
7
Radiomic Parenchymal Phenotypes of Breast Texture from Mammography and Association with Risk of Breast Cancer.乳腺钼靶检查中乳腺纹理的影像组学实质表型及其与乳腺癌风险的关联
Radiology. 2025 May;315(2):e240281. doi: 10.1148/radiol.240281.
8
Relationship between breast tissue involution and breast cancer.乳腺组织退化与乳腺癌之间的关系。
Front Oncol. 2025 Apr 7;15:1420350. doi: 10.3389/fonc.2025.1420350. eCollection 2025.
9
Clinical Application of Artificial Intelligence in Digital Breast Tomosynthesis.人工智能在数字乳腺断层合成中的临床应用
J Korean Soc Radiol. 2025 Mar;86(2):205-215. doi: 10.3348/jksr.2025.0011. Epub 2025 Mar 26.
10
Breast imaging characteristics in Thai transgender women: mammography and ultrasound outcomes in a pilot study.泰国变性女性的乳腺成像特征:一项初步研究中的乳房X线摄影和超声检查结果
Ther Adv Med Oncol. 2025 Mar 22;17:17588359251327984. doi: 10.1177/17588359251327984. eCollection 2025.
基于人群的乳腺癌筛查中的风险评估
J Clin Oncol. 2022 Jul 10;40(20):2279-2280. doi: 10.1200/JCO.21.02827. Epub 2022 Apr 22.
4
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.人工智能在乳腺癌风险的乳腺摄影表型中的应用:叙述性综述。
Breast Cancer Res. 2022 Feb 20;24(1):14. doi: 10.1186/s13058-022-01509-z.
5
Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist.基于深度学习的乳腺 MRI 背景实质增强分类:优于放射科医生。
J Magn Reson Imaging. 2022 Oct;56(4):1068-1076. doi: 10.1002/jmri.28111. Epub 2022 Feb 15.
6
Artificial Intelligence (AI) for Screening Mammography, From the Special Series on AI Applications.人工智能(AI)在乳腺 X 线摄影筛查中的应用,选自 AI 应用专题系列。
AJR Am J Roentgenol. 2022 Sep;219(3):369-380. doi: 10.2214/AJR.21.27071. Epub 2022 Jan 12.
7
Comparing Mammographic Density Assessed by Digital Breast Tomosynthesis or Digital Mammography: The Breast Cancer Surveillance Consortium.数字乳腺断层合成或数字乳腺摄影评估的乳腺密度比较:乳腺癌监测联盟。
Radiology. 2022 Feb;302(2):286-292. doi: 10.1148/radiol.2021204579. Epub 2021 Nov 23.
8
Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model.基于乳腺 X 线摄影的乳腺癌风险模型的多机构验证。
J Clin Oncol. 2022 Jun 1;40(16):1732-1740. doi: 10.1200/JCO.21.01337. Epub 2021 Nov 12.
9
Evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women: a cross-sectional study.中文译文:超声检查乳腺癌中国女性实质模式的证据和评估:一项横断面研究。
BMC Med Imaging. 2021 Oct 19;21(1):152. doi: 10.1186/s12880-021-00687-0.
10
Breast MRI during Neoadjuvant Chemotherapy: Lack of Background Parenchymal Enhancement Suppression and Inferior Treatment Response.新辅助化疗期间的乳腺 MRI:缺乏背景实质增强抑制和治疗反应不佳。
Radiology. 2021 Nov;301(2):295-308. doi: 10.1148/radiol.2021203645. Epub 2021 Aug 24.