Super-resolution Ultrasound Imaging for Differentiating Mummified Thyroid Nodules from Papillary Thyroid Carcinomas.

Authors

Yi Luo, Mingyu Chen, Chengcheng Niu

Published in

Academic radiology. Aug 26, 2026. Epub Aug 26, 2026.

Abstract

Super-resolution ultrasound imaging (SRUS) overcomes the acoustic diffraction limit, enabling improved visualization of the microvascular architecture. This study aimed to evaluate its diagnostic performance in distinguishing mummified thyroid nodules (MTNs) from papillary thyroid carcinomas (PTCs), with the goal of enhancing diagnostic accuracy for MTNs and reducing unnecessary fine-needle aspirations or surgeries.
A total of 63 MTNs and 66 PTCs were enrolled as the MTNs and PTCs groups, respectively. All nodules were examined by conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) to assess features including size, morphology, margins, echogenicity, calcification, double black-and-white halo sign, and CEUS enhancement patterns. SRUS was performed to quantify microvascular parameters within the nodule area, encompassing microvessel density, density fit, microvessel flow velocity (MFV), vascular tortuosity and complexity, and perfusion indices. Logistic regression analysis was employed to identify independent predictors for differentiating MTNs from PTCs.
Quantitative SRUS analysis revealed that PTCs exhibited higher values in multiple parameters compared to MTNs. In contrast, parameters like MFV, standard deviation of MFV (Std Vel) were lower in PTCs (all p < 0.05). Logistic regression identified Roi1% <59.3, Std Vel >3.97, and vessel count <16.5 as independent predictors for MTNs. The combined diagnostic model integrating these three parameters demonstrated excellent performance, with an area under the curve (AUC) of 0.919 (Z = 0.871, 0.966, p < 0.001).
Quantitative SRUS enables effective differentiation between MTNs and PTCs. The multifactorial diagnostic model based on distinct microvascular characteristics demonstrates promising diagnostic performance for identifying MTNs.

PMID:
42648927
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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