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乳腺癌患者腋窝淋巴结的多参数超声评估。

Multiparametric ultrasound assessment of axillary lymph nodes in patients with breast cancer.

机构信息

Radiology Department II, The Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland.

Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.

出版信息

Sci Rep. 2024 Oct 4;14(1):23072. doi: 10.1038/s41598-024-73376-x.

DOI:10.1038/s41598-024-73376-x
PMID:39366984
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11452636/
Abstract

The presence and extent of metastatic disease in axillary lymph nodes (ALNs) in the setting of breast cancer (BC) are important factors for staging and therapy planning. The purpose of this study was to perform a multiparametric sonographic evaluation of ALNs to better differentiate between benign and metastatic nodes. Ninety-nine patients (mean age 54.1 y) with 103 BCs were included in this study, and 103 ALNs were examined sonographically. B-mode parameters, such as size in two dimensions, shape, cortical thickness and capsule outline, were obtained, followed by vascularity assessment via colour Doppler and microflow imaging and stiffness evaluation via shear wave elastography. Postoperative histopathological evaluation was the reference standard. In the statistical analysis, logistic regression and ROC analyses were conducted to search for feature patterns of both types of ALNs to evaluate the prediction qualities of the analysed variables and their combinations. For a cortex larger than 3 mm, without a circumscribed margin of the LN capsule and SWE (E max > 26 kPa), the AUC was 0.823. Multiparametric assessment, which combined conventional US, quantitative SWE and vascularity analysis, was superior to the single-parameter approach in the evaluation of ALNs.

摘要

在乳腺癌 (BC) 中,腋窝淋巴结 (ALN) 中转移性疾病的存在和范围是分期和治疗计划的重要因素。本研究的目的是对 ALN 进行多参数超声评估,以更好地区分良性和转移性淋巴结。本研究纳入了 99 例(平均年龄 54.1 岁)103 例 BC 患者,对 103 个 ALN 进行了超声检查。获得了 B 模式参数,如二维大小、形状、皮质厚度和包膜轮廓,然后通过彩色多普勒和微血流成像评估血管生成,通过剪切波弹性成像评估硬度。术后组织病理学评估为参考标准。在统计分析中,进行了逻辑回归和 ROC 分析,以寻找两种类型的 ALN 的特征模式,评估分析变量及其组合的预测质量。对于皮质厚度大于 3mm、LN 包膜无边界和 SWE(E max > 26kPa)的情况,AUC 为 0.823。多参数评估,结合常规超声、定量 SWE 和血管生成分析,在评估 ALN 方面优于单参数方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/04b8cecaf1a3/41598_2024_73376_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/343524c79999/41598_2024_73376_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/af47fe6de737/41598_2024_73376_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/7b6e5223bb44/41598_2024_73376_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/04b8cecaf1a3/41598_2024_73376_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/343524c79999/41598_2024_73376_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/af47fe6de737/41598_2024_73376_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/7b6e5223bb44/41598_2024_73376_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4f/11452636/04b8cecaf1a3/41598_2024_73376_Fig4_HTML.jpg

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