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Solid Breast Masses: Neural Network Analysis of Vascular Features at Three-dimensional Power Doppler US for Benign or Malignant Classification¹

¹ From the Department of Computer Science and Information Engineering and Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan (R.F.C.); Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan (S.F.H.); Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan (Y.H.L.); Department of Radiology and Clinical Research Institute, Seoul National University Hospital and the Institute of Radiation Medicine, Seoul National University Medical Research Center, 27 Yongon-dong, Chongno-gu, Seoul 110-744, Korea (W.K.M.); and Department of Surgery, Changhua Christian Hospital, Changhua, Taiwan (D.R.C.). Received January 9, 2006; revision requested March 9; revision received April 3; accepted May 9; final version accepted August 1. Address correspondence to W.K.M. (e-mail: moonwk{at}radcom.snu.ac.kr).

Purpose: 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.

Materials and 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.

Conclusion: Three-dimensional power Doppler US images and neural network analysis of features can aid in classification of breast tumors as benign or malignant.

© RSNA, 2007

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