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本文引用的文献

1
The Positive Predictive Values of the Breast Imaging Reporting and Data System (BI-RADS) 4 Lesions and its Mammographic Morphological Features.乳腺影像报告和数据系统(BI-RADS)4类病变的阳性预测值及其乳腺X线摄影形态学特征
Indian J Surg Oncol. 2021 Mar;12(1):182-189. doi: 10.1007/s13193-020-01274-5. Epub 2021 Jan 11.
2
Prediction for Breast Cancer in BI-RADS Category 4 Lesion Categorized by Age and Breast Composition of Women in Songklanagarind Hospital.BI-RADS 4 类乳腺病变中年龄和乳腺组成对女性乳腺癌预测的研究。
Asian Pac J Cancer Prev. 2021 Feb 1;22(2):531-536. doi: 10.31557/APJCP.2021.22.2.531.
3
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.《全球癌症统计数据 2020:全球 185 个国家和地区 36 种癌症的发病率和死亡率估计》。
CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
4
Evaluation of the association between mammographic density and the risk of breast cancer using Quantra software and the BI-RADS classification.使用Quantra软件和BI-RADS分类评估乳腺密度与乳腺癌风险之间的关联。
Medicine (Baltimore). 2020 Nov 13;99(46):e23112. doi: 10.1097/MD.0000000000023112.
5
Correlation of the BI-RADS assessment categories of Papua New Guinean women with mammographic parenchymal patterns, age and diagnosis.巴布亚新几内亚女性的 BI-RADS 评估类别与乳腺实质类型、年龄和诊断的相关性。
J Med Radiat Sci. 2020 Dec;67(4):269-276. doi: 10.1002/jmrs.422. Epub 2020 Sep 16.
6
Subcategorization of Ultrasonographic BI-RADS Category 4: Assessment of Diagnostic Accuracy in Diagnosing Breast Lesions and Influence of Clinical Factors on Positive Predictive Value.超声 BI-RADS 分类 4 级的再分类:诊断乳腺病变的诊断准确性评估及临床因素对阳性预测值的影响。
Ultrasound Med Biol. 2019 May;45(5):1253-1258. doi: 10.1016/j.ultrasmedbio.2018.12.008. Epub 2019 Feb 22.
7
An overview of mammographic density and its association with breast cancer.乳腺密度概述及其与乳腺癌的关系。
Breast Cancer. 2018 May;25(3):259-267. doi: 10.1007/s12282-018-0857-5. Epub 2018 Apr 12.
8
ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.ACR BI-RADS 评估类别 4 细分在诊断性乳房 X 线摄影中的应用:国家乳房 X 线摄影数据库中的利用和结果。
Radiology. 2018 May;287(2):416-422. doi: 10.1148/radiol.2017170770. Epub 2018 Jan 9.
9
Does patient age affect the PPV of ACR BI-RADS Ultrasound categories 4 and 5 in the diagnostic setting?在诊断环境中,患者年龄是否会影响 ACR BI-RADS 超声类别 4 和 5 的 PPV?
Eur Radiol. 2018 Jun;28(6):2492-2498. doi: 10.1007/s00330-017-5203-3. Epub 2018 Jan 4.
10
Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad.乳腺钼靶密度的定性与定量评估:在美国及其他国家的应用
Diagnostics (Basel). 2017 May 31;7(2):30. doi: 10.3390/diagnostics7020030.

患者年龄和乳腺实质密度对乳腺影像报告和数据系统(BIRADS-4)分类的影响。

Effect of patient age and breast parenchymal density on Breast Imaging-Reporting and Data System (BIRADS-4) Subcategorization.

作者信息

Ghunaim Hadeel A

机构信息

Hadeel A Ghunaim, MBBS. Assistant Professor and Consultant Radiologist, Department of Radiology, College of Medicine, Taibah University, Madinah, Saudi Arabia.

出版信息

Pak J Med Sci. 2024 Nov;40(10):2356-2362. doi: 10.12669/pjms.40.10.9552.

DOI:10.12669/pjms.40.10.9552
PMID:39554642
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11568739/
Abstract

BACKGROUND & OBJECTIVES: BI-RADS (Breast Imaging-Reporting and Data System) is a standard radiological risk assessment for breast lesions, including six categories, of which category four is the widest range of likelihood cancer risk (2% to 95%). This study aimed to evaluate the effect of patient age and ACR breast density on the positive predictive value (PPV) of BI-RADS 4 subcategorization (4A, 4B, and 4C).

METHODS

A retrospective study was conducted at King Fahed Hospital (KFH) between September 1, 2021, and June 30, 2022. PPV was calculated based on a histopathological report for all lesions. The correlation was made with patients' age groups ranging from < 45 years, 45-55 years, and > 55 years and four types of breast density on mammography. We used IBM-SPSS for data synthesis. The Chi-square test was used to assess any correlation between variables. A p-value < 0.05 was considered statistically significant.

RESULTS

The mean age was 44.62 ±14.32. Of the 248 cases, 81% were benign, 17% were malignant, and 2% were high-risk lesions. The age-related PPV of each BI-RADS category showed no significant differences among all age groups. However, the breast composition-related PPV cases with BI-RADS categories differed significantly within subcategory 4C (p-value<0.001).

CONCLUSIONS

There were no positive relationships between increasing age and PPV in BI-RADS 4 subcategories 4A, B, and C. Breast composition-related PPV showed significant differences with BI-RADS 4C subcategories.

摘要

背景与目的

乳腺影像报告和数据系统(BI-RADS)是用于乳腺病变的标准放射学风险评估,包括六个类别,其中四类的癌症风险可能性范围最广(2%至95%)。本研究旨在评估患者年龄和ACR乳腺密度对BI-RADS 4类细分(4A、4B和4C)的阳性预测值(PPV)的影响。

方法

于2021年9月1日至2022年6月30日在法赫德国王医院(KFH)进行了一项回顾性研究。基于所有病变的组织病理学报告计算PPV。将其与年龄组(小于45岁、45 - 55岁和大于55岁)以及乳腺钼靶检查的四种乳腺密度类型进行关联分析。我们使用IBM-SPSS进行数据综合分析。采用卡方检验评估变量之间的任何相关性。p值<0.05被认为具有统计学意义。

结果

平均年龄为44.62±14.32。在248例病例中,81%为良性,17%为恶性,2%为高危病变。每个BI-RADS类别的年龄相关PPV在所有年龄组之间均无显著差异。然而,BI-RADS类别中与乳腺组成相关的PPV病例在4C亚类别内存在显著差异(p值<0.001)。

结论

在BI-RADS 4类的4A、4B和4C亚类别中,年龄增长与PPV之间不存在正相关关系。与乳腺组成相关的PPV在BI-RADS 4C亚类别中存在显著差异。