|
|||||
|
|
Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images |
|
| Authors: | Yan-Hao Huang Jeon-Hor Chen Yeun-Chung Chang Chiun-Sheng Huang Woo Kyung Moon Wen-Jia Kuo Kuan-Ju Lai Ruey-Feng Chang |
| Institution: | 1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 106, Republic of China 2. Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA 3. Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan, Republic of China 4. Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China 5. Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan, Republic of China 6. Department of Diagnostic Radiology, College of Medicine, Seoul National University Hospital, Seoul, South Korea 7. Department of Information Management, Yuan Ze University, Chung-Li, Taiwan, Republic of China 8. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, Republic of China 9. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China |
| 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?AZ (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 AZ values between the proposed method and conventional vascularity index method using z test was 0.04. |
| Keywords: | 3-D ultrasound Power Doppler ultrasound Breast tumor Vascularity |
| 本文献已被 SpringerLink 等数据库收录！ | |