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弹性成像提高了传统超声在鉴别浅表淋巴结病变良恶性方面的诊断性能。

Elastography Enhances the Diagnostic Performance of Conventional Ultrasonography in Differentiating Benign from Malignant Superficial Lymphadenopathies.

作者信息

Pugliese Novella, Picardi Marco, Giordano Claudia, Vincenzi Annamaria, Cappiello Rosaria, Mascolo Massimo, Pane Fabrizio

机构信息

Department of Clinical Medicine and Surgery, Hematology Section, University of Naples "Federico II", Via Sergio Pansini, 5, 80131 Naples, Italy.

Department of Advanced Biomedical Sciences, Pathology Section, University of Naples "Federico II", Via Sergio Pansini, 5, 80131 Naples, Italy.

出版信息

Cancers (Basel). 2025 Apr 28;17(9):1480. doi: 10.3390/cancers17091480.

DOI:10.3390/cancers17091480
PMID:40361407
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12071056/
Abstract

BACKGROUND/OBJECTIVES: Lymph node (LN) evaluation is critical in diagnosing, staging, and managing various diseases, particularly lymphoma and metastatic cancer. Although conventional ultrasound (US) is widely used for this purpose, its limitations in reliably differentiating between benign and malignant LNs persist. Ultrasound elastography (US-E), which evaluates tissue stiffness, has emerged as a promising adjunct to improve diagnostic accuracy. This study aims to evaluate the diagnostic performance of conventional US, power Doppler US, and strain elastography (SE) in distinguishing malignant from benign superficial lymph nodes.

METHODS

In this prospective study, 214 consecutive patients referred for US of enlarged LNs were enrolled. Conventional B-mode US, power Doppler, and SE were performed, and the strain ratio (SR) was calculated as a measure of LN stiffness. Histopathological examination was used as the reference standard. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) analysis, and multivariable logistic regression models were applied to determine the independent predictive role of SR.

RESULTS

Among the 214 LNs (one for each patient), 74 (34.6%) were benign and 140 (65.4%) were malignant. The SR showed a significant association with malignancy ( < 0.001). For hematological malignancies, SR demonstrated high sensitivity (79-85%) and specificity (81-96%), with an overall area under the curve (AUC) of 0.91. Multivariable analysis confirmed that SR was an independent predictor of malignancy (continuous and dichotomous), with a 14% gain in predictive accuracy when treated as a continuous variable ( < 0.0001).

CONCLUSIONS

US-E, particularly SR, is a valuable tool in the differentiation of benign and malignant superficial LNs. SR provides significant diagnostic value, especially in hematological neoplasms like Hodgkin lymphoma, and can serve as an independent predictor of malignancy. This technique, when used in combination with conventional US features, offers enhanced diagnostic performance for LN evaluation.

摘要

背景/目的:淋巴结(LN)评估对于各种疾病的诊断、分期和管理至关重要,尤其是淋巴瘤和转移性癌症。尽管传统超声(US)广泛用于此目的,但其在可靠区分良性和恶性LN方面的局限性依然存在。评估组织硬度的超声弹性成像(US-E)已成为提高诊断准确性的一种有前景的辅助手段。本研究旨在评估传统US、能量多普勒US和应变弹性成像(SE)在区分恶性与良性浅表淋巴结方面的诊断性能。

方法

在这项前瞻性研究中,纳入了214例因LN肿大而转诊接受US检查的连续患者。进行了传统B型US、能量多普勒和SE检查,并计算应变比(SR)作为LN硬度的指标。组织病理学检查用作参考标准。使用受试者操作特征(ROC)分析评估诊断准确性,并应用多变量逻辑回归模型确定SR的独立预测作用。

结果

在214个LN(每位患者1个)中,74个(34.6%)为良性,140个(65.4%)为恶性。SR与恶性肿瘤显著相关(<0.001)。对于血液系统恶性肿瘤,SR显示出高敏感性(79 - 85%)和特异性(81 - 96%),曲线下总面积(AUC)为0.91。多变量分析证实,SR是恶性肿瘤的独立预测因子(连续和二分变量),当作为连续变量处理时,预测准确性提高了14%(<0.0001)。

结论

US-E,尤其是SR,是区分良性和恶性浅表LN的有价值工具。SR具有显著的诊断价值,特别是在霍奇金淋巴瘤等血液系统肿瘤中,并且可以作为恶性肿瘤的独立预测因子。该技术与传统US特征联合使用时,可提高LN评估的诊断性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a37/12071056/15ee77f1db4b/cancers-17-01480-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a37/12071056/f799f02e2a35/cancers-17-01480-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a37/12071056/1a3f413f0d47/cancers-17-01480-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a37/12071056/15ee77f1db4b/cancers-17-01480-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a37/12071056/f799f02e2a35/cancers-17-01480-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a37/12071056/1a3f413f0d47/cancers-17-01480-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a37/12071056/15ee77f1db4b/cancers-17-01480-g003.jpg

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