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

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.