title: Solid breast masses: neural network analysis of vascular features at three-dimensional power Doppler US for benign or malignant classification.
abstract: OBJECTIVE
  To retrospectively evaluate the accuracy of neural network analysis of tumor vascular features at three-dimensional (3D) power Doppler ultrasonography (US) for classification of breast tumors as benign or malignant, with histologic findings as the reference standard.
  
  
  METHODS
  This study was approved by the local ethics committee; informed consent was waived. Three-dimensional power Doppler US images of 221 solid breast masses (110 benign, 111 malignant) were obtained in 221 women (mean age, 46 years; range, 25-71 years). After narrowing down vessels to skeletons with a 3D thinning algorithm, six vascular feature values--vessel-to-volume ratio, number of vascular trees, total vessel length, longest path length, number of bifurcations, and vessel diameter-were computed. A neural network was used to classify tumors by using these features. Independent-samples t test and receiver operating characteristic (ROC) curve analysis were used.
  
  
  RESULTS
  Mean values of vessel-to-volume ratio, number of vascular trees, total vessel length, longest path length, number of bifurcations, and vessel diameter were 0.0089 +/- 0.0073 (standard deviation), 26.41 +/- 14.73, 23.02 cm +/- 19.53, 8.44 cm +/- 10.38, 36.31 +/- 37.06, and 0.088 cm +/- 0.021 in malignant tumors, respectively, and 0.0028 +/- 0.0021, 9.69 +/- 6.75, 5.17 cm +/- 4.78, 1.68 cm +/- 1.79, 6.05 +/- 7.55, and 0.064 cm +/- 0.028 in benign tumors, respectively (P < .001 for all six features). Area under ROC curve (A(z)) values of the six features were 0.84, 0.87, 0.87, 0.82, 0.84, and 0.75, respectively. Accuracy, sensitivity, specificity, and positive and negative predictive values were 85% (187 of 221), 83% (96 of 115), 86% (91 of 106), 86% (96 of 111), and 83% (91 of 110), respectively, with A(z) of 0.92 based on all six feature values.
  
  
  CONCLUSIONS
  Three-dimensional power Doppler US images and neural network analysis of features can aid in classification of breast tumors as benign or malignant.
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
authors.forename: Sheng-Fang
authors.initials: SF
authors.lastname: Huang
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
mesh_terms: D000328:Adult; D000368:Aged; D001808:Blood Vessels / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D001940:Breast / Q000098:blood supply*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D018608:Ultrasonography, Doppler