The value of multimodal ultrasound in diagnosis of cervical lymphadenopathy: can real-time elastography help identify benign and malignant lymph nodes?

Autor: Jiahui Tong, Ting Lin, Boping Wen, Peijun Chen, Ying Wang, Yuehui Yu, Menghan Chen, Gaoyi Yang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Frontiers in Oncology, Vol 13 (2023)
Druh dokumentu: article
ISSN: 2234-943X
DOI: 10.3389/fonc.2023.1073614
Popis: AimTo investigate the multimodal ultrasound(MMUS) features of cervical lymphadenopathy and to assess its value in the differential diagnosis of benign and malignant cervical lymph nodes.MethodsA retrospective analysis of 169 patients with cervical lymph node enlargement who attended Hangzhou Red Cross Hospital from March 2020 to October 2022. All patients underwent conventional ultrasound (CUS), contrast-enhanced ultrasound (CEUS), and real-time elastography (RTE), and were divided into training set and validation set. Univariate analysis was applied to screen out statistically significant parameters, and CUS model and MMUS model were constructed by multifactorial logistic regression analysis. The receiver operator characteristic (ROC) curve was established, and the area under the curve (AUC) was used to compare CUS model with MMUS model to assess the value of MMUS.ResultsOf the cervical 169 lymph nodes in 169 patients included in the study. The 169 enrolled patients were divided into a training set (132 patients) and a validation set (37 patients). In the training set, univariate analysis showed statistically significant differences in long diameter/short diameter(L/S), border, margin, hilus, dermal medulla boundary, blood flow type, enhancement mode, enhancement type, and RTE score (all p< 0.05). Multifactor logistic analysis showed that L/S, blood flow type, enhancement mode and enhancement type were correlates of malignant lymph nodes (all p< 0.05). The comparison of AUC demonstrated that the discriminative ability of the MMUS model was superior to using the CUS model, both in the training set(p = 0.004) and validation set (p
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