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通过超微血管造影术(UMA)检测的微血管生物标志物有助于识别具有意义未明异型性的甲状腺乳头状癌(PTC)。

Biomarkers of microvascularture by ultra Micro-angiography (UMA) assist to identify papillary thyroid carcinoma (PTC) with atypia of undetermined significance.

作者信息

Wang Qingsong, Li Zhewei, Zhang Jie, Zhang Sijie, Wang Lijun, Yao Hongjian, Zhang Hong, Li Jing, Wang Shuo, Sun Jinglai, Zhang Wenhui, Yu Hui

机构信息

Department of Biomedical Engineering, Tianjin University School of Medicine, Tianjin, China.

State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University School of Medicine, Tianjin, China.

出版信息

BMC Cancer. 2025 May 1;25(1):819. doi: 10.1186/s12885-025-14197-7.

Abstract

BACKGROUND

It is challenging to identify Papillary Thyroid Cancer (PTC) which shows atypia of undetermined significance (AUS) by Fine-needle Aspiration (FNA). This study aims to seek the meaningful quantitative biomarkers of the microvasculature and construct a classification model for PTC with AUS based on these new biomarkers and Thyroid Imaging Reporting and Data System (TI-RADS).

METHODS

This prospective study enrolled 281 patients with 300 thyroid nodules showing AUS. These cases were divided into two groups with the largest dimension (LD) of 10 mm, A (< 10 mm) and B (≥ 10 mm). Firstly, an open-source artifact suppression algorithm, which combined a multi-scale Frangi filter and TOPHAT operation, was proposed for the segmentation of micro-vessels in Ultra Micro-Angiography (UMA) images. Then, 18 quantitative biomarkers were calculated and analyzed through Mann-Whitney test (U-test), while LASSO regression was utilized to remove collinear features. Finally, two different classification models were built using logistic regression through the selected biomarkers combined with Chinese TI-RADS (C TI-RADS) or American College of Radiology TI-RADS (ACR TI-RADS). The performances were evaluated using the mean Area Under the Curve (AUC) value and the DeLong test, through a 5-fold cross-validation experiment.

RESULTS

Group A comprised 58 benign nodules and 104 PTCs, while Group B consisted of 60 benign nodules and 78 PTCs. Four biomarkers were selected in Group A. The 5-fold cross-validation experiment showed that the mean Area Under Curve (AUC) improved from 0.725 with ACR TI-RADS to 0.851 (P < 0.05), while the mean AUC improved from 0.809 with C TI-RADS to 0.882 (P < 0.05). In Group B, four different biomarkers were selected, and the classification models showed improvements from 0.841 with ACR TI-RADS to 0.874 and from 0.894 with C TI-RADS to 0.936.

CONCLUSIONS

This study demonstrated the potential value of microvasculature in the prediction of PTC in AUS Cases and improved the performance of ultrasound examination. Moreover, the morphology of microvasculature showed different changes at different LD groups.

摘要

背景

通过细针穿刺活检(FNA)来鉴别显示意义不明确的非典型性(AUS)的甲状腺乳头状癌(PTC)具有挑战性。本研究旨在寻找有意义的微血管定量生物标志物,并基于这些新的生物标志物和甲状腺影像报告和数据系统(TI-RADS)构建AUS的PTC分类模型。

方法

这项前瞻性研究纳入了281例有300个显示AUS的甲状腺结节的患者。这些病例根据最大径(LD)分为两组,A组(<10mm)和B组(≥10mm)。首先,提出了一种结合多尺度Frangi滤波器和TOPHAT操作的开源伪影抑制算法,用于超微血管造影(UMA)图像中的微血管分割。然后,计算并通过曼-惠特尼检验(U检验)分析18个定量生物标志物,同时利用LASSO回归去除共线特征。最后,通过选定的生物标志物结合中国TI-RADS(C TI-RADS)或美国放射学会TI-RADS(ACR TI-RADS),使用逻辑回归建立两种不同的分类模型。通过5折交叉验证实验,使用平均曲线下面积(AUC)值和德龙检验来评估性能。

结果

A组包括58个良性结节和104个PTC,而B组由60个良性结节和78个PTC组成。A组中选择了4个生物标志物。5折交叉验证实验表明,平均曲线下面积(AUC)从ACR TI-RADS的0.725提高到0.851(P<0.05),而平均AUC从C TI-RADS的0.809提高到0.882(P<0.05)。在B组中,选择了4个不同的生物标志物,分类模型从ACR TI-RADS的0.841提高到0.874,从C TI-RADS的0.894提高到0.936。

结论

本研究证明了微血管在AUS病例中PTC预测中的潜在价值,并提高了超声检查的性能。此外,微血管形态在不同LD组显示出不同的变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52a5/12044894/dc500401ee01/12885_2025_14197_Fig1_HTML.jpg

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