Title
Diagnosis of solid breast tumors using vessel analysis in three-dimensional power Doppler ultrasound images.
Abstract
This study aims to evaluate whether the distribution of vessels inside and adjacent to tumor region at three-dimensional (3-D) power Doppler ultrasonography (US) can be used for the differentiation of benign and malignant breast tumors. 3-D power Doppler US images of 113 solid breast masses (60 benign and 53 malignant) were used in this study. Blood vessels within and adjacent to tumor were estimated individually in 3-D power Doppler US images for differential diagnosis. Six features including volume of vessels, vascularity index, volume of tumor, vascularity index in tumor, vascularity index in normal tissue, and vascularity index in surrounding region of tumor within 2 cm were evaluated. Neural network was then used to classify tumors by using these vascular features. The receiver operating characteristic (ROC) curve analysis and Student's t test were used to estimate the performance. All the six proposed vascular features are statistically significant (p < 0.001) for classifying the breast tumors as benign or malignant. The A Z (area under ROC curve) values for the classification result were 0.9138. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis performance based on all six proposed features were 82.30 (93/113), 86.79 (46/53), 78.33 (47/60), 77.97 (46/59), and 87.04 % (47/54), respectively. The p value of A Z values between the proposed method and conventional vascularity index method using z test was 0.04.
Year
DOI
Venue
2013
10.1007/s10278-012-9556-5
J. Digital Imaging
Keywords
Field
DocType
roc curve,predictive value of tests
Ultrasonography,Receiver operating characteristic,Vascularity,Ultrasonography doppler,Predictive value of tests,Radiology,Doppler effect,Power doppler ultrasound,Medicine,Differential diagnosis
Journal
Volume
Issue
ISSN
26
4
1618-727X
Citations 
PageRank 
References 
0
0.34
4
Authors
8
Name
Order
Citations
PageRank
Yan-Hao Huang181.33
Jeon-Hor Chen21285.51
Yeun-Chung Chang3365.49
Chiun-Sheng Huang41539.33
Woo Kyung Moon529919.46
Wen-Jia Kuo6393.09
Kuan-Ju Lai700.34
Ruey-Feng Chang839534.88