Superresolution US Imaging of Rabbit Lymph Node Microvasculature for Metastasis Diagnosis.

Purpose To compare the diagnostic performance of superresolution US (SRUS) with that of US and contrast-enhanced US (CEUS) for detecting lymph node (LN) metastasis in rabbits. Materials and Methods US, CEUS, and SRUS were performed in 30 rabbits with 16 egg yolk-induced hyperplastic and 14 VX2 tumor-bearing metastatic LNs. Qualitative US, CEUS, and SRUS features and quantitative SRUS-based parameters of LN microvasculature were analyzed. Major qualitative features and quantitative parameters associated with metastatic LNs were identified, with histopathologic evaluation as reference. Spearman correlation between features and parameters was analyzed. Results Among qualitative features, chaotic microvasculature at SRUS achieved the highest area under the receiver operating characteristic curve of 0.92 (95% CI: 0.81, 1.00), outperforming cortical thickening at US (0.67 [95% CI: 0.51, 0.82]; P = .012), arterial phase enhancing pattern at CEUS (0.72 [95% CI: 0.57, 0.86]; P = .018), and SRUS vascular pattern (0.72 [95% CI: 0.55, 0.89]; P = .028). Among quantitative parameters, fractal dimension yielded the optimal area under the receiver operating characteristic curve of 0.92 (95% CI: 0.79, 1.00), outperforming vessel density (0.70 [95% CI: 0.48, 0.88]; P = .046), flow-weighted vessel density (0.70 [95% CI: 0.49, 0.88]; P = .046), and direction entropy (0.71 [95% CI: 0.51, 0.89]; P = .046). Chaotic microvasculature and fractal dimension were strongly correlated (ρ = 0.80; P < .001), and both correlated well with microvascular density (ρ = 0.82 and 0.65, respectively; both P < .001). Conclusion Chaotic microvasculature and fractal dimension assessed at SRUS showed improved diagnostic performance for detecting metastatic LNs in rabbits compared with US and CEUS and enabled noninvasive evaluation of microvascular tortuosity. Keywords: Ultrasound-Contrast, Ultrasound Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.
Cancer
Care/Management

Authors

Ye Ye, Li Li, Peng Peng, Li Li, Yang Yang, He He, Meng Meng, Li Li, Hu Hu, Li Li, Zhou Zhou
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