Suppr超能文献

第五版 BI-RADS 超声词汇在 4 类乳腺病变中的应用:中国前瞻性多中心研究。

The Utility of the Fifth Edition of the BI-RADS Ultrasound Lexicon in Category 4 Breast Lesions: A Prospective Multicenter Study in China.

机构信息

Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China.

Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

出版信息

Acad Radiol. 2022 Jan;29 Suppl 1:S26-S34. doi: 10.1016/j.acra.2020.06.027. Epub 2020 Aug 4.

Abstract

RATIONALE AND OBJECTIVES

The objective of this study was to evaluate the utility of the fifth edition of the Breast Imaging-Reporting and Data System (BI-RADS) in clinical breast radiology by using prospective multicenter real-time analyses of ultrasound (US) images.

MATERIALS AND METHODS

We prospectively studied 2049 female patients (age range, 19-86 years; mean age 46.88 years) with BI-RADS category 4 breast masses in 32 tertiary hospitals. All the patients underwent B-mode, color Doppler US, and US elastography examination. US features of the mass and associated features were described and categorized according to the fifth edition of the BI-RADS US lexicon. The pathological results were used as the reference standard. The positive predictive values (PPVs) of subcategories 4a-4c were calculated.

RESULTS

A total of 2094 masses were obtained, including 1124 benign masses (54.9%) and 925 malignant masses (45.1%). For BI-RADS US features of mass shape, orientation, margin, posterior features, calcifications, architectural distortion, edema, skin changes, vascularity, and elasticity assessment were significantly different for benign and malignant masses (p< 0.05). Typical signs of malignancy were irregular shape (PPV, 57.2%), spiculated margin (PPV, 83.7%), nonparallel orientation (PPV, 63.9%), and combined pattern of posterior features (PPV, 60.6%). For the changed or newly added US features, the PPVs for intraductal calcifications were 80%, 56.4% for internal vascularity, and 80% for a hard pattern on elastography. The associated features such as architectural distortion (PPV, 89.3%), edema (PPV, 69.2%), and skin changes (PPV, 76.2%) displayed high predictive value for malignancy. The rate of malignant was 7.4% (72/975) in category 4a, 61.4% (283/461) in category 4b, and 93.0% (570/613) in category 4c. The PPV for category 4b was higher than the likelihood ranges specified in BI-RADS and the PPVs for categories 4a and 4c were within the acceptable performance ranges specified in the fifth edition of BI-RADS in our study.

CONCLUSION

Not only the US features of the breast mass, but also associated features, including vascularity and elasticity assessment, have become an indispensable part of the fifth edition of BI-RADS US lexicon to distinguish benign and malignant breast lesions. The subdivision of category 4 lesions into categories 4a, 4b, and 4c for US findings is helpful for further assessment of the likelihood of malignancy of breast lesions.

摘要

背景与目的

本研究旨在通过对超声(US)图像进行前瞻性多中心实时分析,评估第五版乳腺影像报告和数据系统(BI-RADS)在临床乳腺放射学中的应用。

材料与方法

我们前瞻性研究了 32 家三级医院的 2049 名 BI-RADS 4 类乳腺肿块的女性患者(年龄 19-86 岁,平均年龄 46.88 岁)。所有患者均接受 B 型、彩色多普勒 US 和 US 弹性成像检查。根据第五版 BI-RADS US 词汇表描述和分类肿块的 US 特征及相关特征。将病理结果作为参考标准。计算亚类 4a-4c 的阳性预测值(PPV)。

结果

共获得 2094 个肿块,其中良性肿块 1124 个(54.9%),恶性肿块 925 个(45.1%)。对于 BI-RADS US 肿块形状、方位、边缘、后方特征、钙化、结构扭曲、水肿、皮肤改变、血管生成和弹性评估等特征,良性和恶性肿块之间存在显著差异(p<0.05)。恶性肿瘤的典型特征为不规则形状(PPV 57.2%)、毛刺状边缘(PPV 83.7%)、非平行方位(PPV 63.9%)和后方特征综合模式(PPV 60.6%)。对于改变或新增的 US 特征,导管内钙化的 PPV 为 80%,内部血管生成的 PPV 为 56.4%,弹性成像的硬模式为 80%。结构扭曲(PPV 89.3%)、水肿(PPV 69.2%)和皮肤改变(PPV 76.2%)等相关特征对恶性肿瘤具有较高的预测价值。在 4a 类中,恶性的发生率为 7.4%(72/975),在 4b 类中为 61.4%(283/461),在 4c 类中为 93.0%(570/613)。4b 类的 PPV 高于 BI-RADS 中指定的可能性范围,4a 类和 4c 类的 PPV 均在第五版 BI-RADS 中指定的可接受性能范围内。

结论

不仅是乳腺肿块的 US 特征,包括血管生成和弹性评估在内的相关特征,也已成为第五版 BI-RADS US 词汇表中不可或缺的一部分,用于区分良性和恶性乳腺病变。根据 US 表现将 4 类病变细分为 4a、4b 和 4c 亚类有助于进一步评估乳腺病变恶性的可能性。

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