Anush Sridharan1, John R Eisenbrey2, Maria Stanczak2, Priscilla Machado2, Daniel A Merton2, Annina Wilkes2, Alexander Sevrukov2, Haydee Ojeda-Fournier3, Robert F Mattrey3, Kirk Wallace4, Flemming Forsberg5. 1. Department of Radiology, Thomas Jefferson University, 763H Main Building, 132 South 10th Street, Philadelphia, PA 19107; Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania. 2. Department of Radiology, Thomas Jefferson University, 763H Main Building, 132 South 10th Street, Philadelphia, PA 19107. 3. Department of Radiology, University of California, San Diego, California. 4. GE Global Research, Niskayuna, New York. 5. Department of Radiology, Thomas Jefferson University, 763H Main Building, 132 South 10th Street, Philadelphia, PA 19107. Electronic address: flemming.forsberg@jefferson.edu.
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.
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.
Authors: Kenneth J W Taylor; Christopher Merritt; Catherine Piccoli; Robert Schmidt; Glenn Rouse; Bruno Fornage; Eva Rubin; Dianne Georgian-Smith; Fred Winsberg; Barry Goldberg; Ellen Mendelson Journal: Ultrasound Med Biol Date: 2002-01 Impact factor: 2.998
Authors: N Weidner; J Folkman; F Pozza; P Bevilacqua; E N Allred; D H Moore; S Meli; G Gasparini Journal: J Natl Cancer Inst Date: 1992-12-16 Impact factor: 13.506