Identification of superficial invasive and indolent lymphomatous lymph nodes by multiple ultrasonographic vascular imaging

淋巴 鉴定(生物学) 超声科 血管侵犯 医学 病理 解剖 放射科 生物 癌症 内科学 植物
作者
Lin Li,Wenjuan Lu,Hongyan Deng,Wenqin Chen,Hua Shu,Pingyang Zhang,Xinhua Ye
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:15 (1): 9886-9886
标识
DOI:10.1038/s41598-025-93545-w
摘要

This study aimed to explore whether superficial invasive lymphomas and indolent lymphomas could be identified by Ultrasonographic vascular imaging. A retrospective study enrolled 82 lymphoma patients. According to proliferation rates and clinical course, the lymph nodes were classified as invasive and indolent lymphomatous lymph nodes. All patients underwent ultrasound (US) with three established techniques: color Doppler flow imaging (CDFI), angio plus ultrasound imaging (AngioPLUS™), and contrast-enhanced ultrasound (CEUS). Qualitative and quantitative parameters from the two groups were compared. Finally, the area under the receiver-operating characteristic (ROC) and regression analysis were used to compare the differences between the two groups and determine the diagnostic efficiency of the three techniques for differentiating invasive lymphoma from indolent lymphoma. The types of blood flow distribution between invasive and indolent lymphomatous lymph nodes were statistically different in all three Ultrasound techniques. In CDFI, invasive or indolent lymphomatous lymph nodes were determined by resistance index (RI) (p < 0.001). AngioPLUSTM offered better blood flow performance and diagnostic sensitivity than CDFI. In CEUS, the differences between the two groups in necrosis and arrival time (ATM) (p = 0.026, 0.043) were statistically significant. Finally, CDFI combined with CEUS had the highest diagnostic sensitivity of 98.1%. Interobserver agreements for qualitative parameters were all excellent. Ultrasonographic Vascular imaging is useful in identifying invasive and indolent lymphomatous lymph nodes, and CDFI combined with CEUS had the highest diagnostic sensitivity, which can guide clinicians to make more accurate diagnosis and better treatment for patients.
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