The clinical value of Arrival-time Parametric Imaging using contrast-enhanced ultrasonography in differentiating benign and malignant breast lesions

Abstract

OBJECTIVES:

To evaluate the clinical value of Arrival-time Parametric Imaging (At-PI) in the differentiation of benign and malignant breast lesions.

METHODS:

For this ethics committee-approved retrospective study, a total of 184 breast lesions in 176 women were included and gray-scale ultrasound, contrast-enhanced ultrasound (CEUS) and At-PI were performed. In CEUS and At-PI, perfusion patterns, perfusion uniformity and color spatial distribution for lesions were analyzed qualitatively and the maximal diameter ratio of the lesion in accumulated parametric images and that in gray-scale images (MDR_AI/GI) and area ratio of the lesion in accumuated parametric images and that in gray-scale images (AR_AI/GI) were calculated quantitatively. Kappa and Intraclass Correlation Coefficient were used to evaluate the interobserver reproducibility for CEUS and At-PI and the intraobserver reproducibility for At-PI, respectively. The area under receiver operating characteristic (AUC), sensitivity, specificity, accuracy and positive and negative likelihood ratios (PPV, NPV) were calculated for MDR_AI/GI and AR_AI/GI.

RESULTS:

Good interobserver and intraobserver reproducibility for At-PI were identified. In At-PI, there were statistically significant differences in perfusion patterns, color spatial distribution, MDR_AI/GI and AR_AI/GI between benign and malignant breast lesions (P < 0.05). The AUCs of MDR_AI/GI and AR_AI/GI were 0.895 and 0.954, respectively, with no significant difference between them (Z = 1.84, P > 0.05). By using the thresholds of 1.125 for MDR_AI/GI and 1.21 for AR_AI/GI, the sensitivity, specificity, accuracy, PPV and NPV of At-PI were 84.48%, 88.24%, 85.57%, 92.45% and 76.92%, respectively, for MDR_AI/GI and 93.10%, 91.18%, 92.39%, 94.74% and 88.57%, respectively, for AR_AI/GI.

CONCLUSIONS:

At-PI is helpful to distinguish benign from malignant breast lesions. And MDR_AI/GI and AR_AI/GI are useful and efficient features for differential diagnosis.