Characterizing Breast Lesions Using Quantitative Parametric 3D Subharmonic Imaging: A Multicenter Study.
Publication/Presentation Date
8-1-2020
Abstract
RATIONALE AND OBJECTIVES: Breast cancer is the leading type of cancer among women. Visualization and characterization of breast lesions based on vascularity kinetics was evaluated using three-dimensional (3D) contrast-enhanced ultrasound imaging in a clinical study.
MATERIALS AND METHODS: Breast lesions (n = 219) were imaged using power Doppler imaging (PDI), 3D contrast-enhanced harmonic imaging (HI), and 3D contrast-enhanced subharmonic imaging (SHI) with a modified Logiq 9 ultrasound scanner using a 4D10L transducer. Quantitative metrics of vascularity derived from 3D parametric volumes (based on contrast perfusion; PER and area under the curve; AUC) were generated by off-line processing of contrast wash-in and wash-out. Diagnostic accuracy of these quantitative vascular parameters was assessed with biopsy results as the reference standard.
RESULTS: Vascularity was observed with PDI in 93 lesions (69 benign and 24 malignant), 3D HI in 8 lesions (5 benign and 3 malignant), and 3D SHI in 83 lesions (58 benign and 25 malignant). Diagnostic accuracy for vascular heterogeneity, PER, and AUC ranged from 0.52 to 0.75, while the best logistical regression model (vascular heterogeneity ratio, central PER, and central AUC) reached 0.90.
CONCLUSION: 3D SHI successfully detects contrast agent flow in breast lesions and characterization of these lesions based on quantitative measures of vascular heterogeneity and 3D parametric volumes is promising.
Volume
27
Issue
8
First Page
1065
Last Page
1074
ISSN
1878-4046
Published In/Presented At
Sridharan, A., Eisenbrey, J. R., Stanczak, M., Machado, P., Merton, D. A., Wilkes, A., Sevrukov, A., Ojeda-Fournier, H., Mattrey, R. F., Wallace, K., & Forsberg, F. (2020). Characterizing Breast Lesions Using Quantitative Parametric 3D Subharmonic Imaging: A Multicenter Study. Academic radiology, 27(8), 1065–1074. https://doi.org/10.1016/j.acra.2019.10.029
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
31859210
Department(s)
Administration and Leadership
Document Type
Article