title: Analysis of tumor vascularity using three-dimensional power Doppler ultrasound images.
abstract: Tumor vascularity is an important factor that has been shown to correlate with tumor malignancy and was demonstrated as a prognostic indicator for a wide range of cancers. Three-dimensional (3-D) power Doppler ultrasound (PDUS) offers a convenient tool for investigators to inspect the signals of blood flow and vascular structures in breast cancer. In this paper, a new computer-aided diagnosis (CAD) system for quantifying Doppler ultrasound images based on 3-D thinning algorithm and neural network is proposed. We extracted the skeleton of blood vessels from 3-D PDUS data to facilitate the capturing of morphological changes. Nine features including vessel-to-volume ratio, number of vascular trees, length of vessels, number of branching, mean of radius, number of cycles, and three tortuosity measures, were extracted from the thinning result. Benign and malignant tumors can therefore be differentiated by a score computed by a multilayered perceptron (MLP) neural network using these features as parameters. The proposed system was tested on 221 breast tumors, including 110 benign and 111 malignant lesions. The accuracy, sensitivity, specificity, and positive and negative predictive values were 88.69% (196/221), 91.89% (102/111), 85.45% (94/110), 86.44% (102/118), and 91.26% (94/103), respectively. The Az value of the ROC curve was 0.94. The results demonstrate a correlation between the morphology of blood vessels and tumor malignancy, indicating that the newly proposed method can retrieves a high accuracy in the classification of benign and malignant breast tumors.
authors.affiliation: Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan 970, ROC. sfhuang@mail.tcu.edu.tw
authors.forename: Sheng-Fang
authors.initials: SF
authors.lastname: Huang
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
authors.forename: Woo Kyung
authors.initials: WK
authors.lastname: Moon
authors.forename: Yu-Hau
authors.initials: YH
authors.lastname: Lee
authors.forename: Dar-Ren
authors.initials: DR
authors.lastname: Chen
authors.forename: Jasjit S
authors.initials: JS
authors.lastname: Suri
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009369:Neoplasms / Q000098:blood supply* / Q000000981:diagnostic imaging*; D009389:Neovascularization, Pathologic / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018608:Ultrasonography, Doppler / Q000379:methods*