ANALYSIS OF ELASTOGRAPHIC AND B-MODE FEATURES AT SONOELASTOGRAPHY FOR BREAST TUMOR CLASSIFICATION

The purpose of this study was to evaluate the accuracy of neural network analysis of elastographic features at sonoelastography for the classification of biopsy-proved benign and malignant breast tumors. Sonoelastography of 181 solid breast masses (113 benign and 68 malignant tumors) was performed for 181 patients (mean age, 47 years; range, 24-75 years). After the manual segmentation of the tumors, five elastographic features (strain difference, strain ratio, mean, median and mode) and six B-mode features (orientation, undulation, angularity, average gradient, gradient variance and intensity variance) were computed. A neural network was used to classify tumors by the use of these features. The Student`s t test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. Area under ROC curve (Az) values of the three elastographic features mean (0.87), median (0.86) and mode (0.83)-were significantly higher than the Az values for the six B-mode features (0.54-0.69) (p < 0.01). Accuracy, sensitivity, specificity and Az of the neural network for the classification of solid breast tumors were 86.2% (156/181), 83.8% (57/68), 87.6% (99/113) and 0.84 for the elastographic features, respectively, and 82.3% (149/181), 70.6% (48/68), 89.4% (101/113) and 0.78 for the B-mode features, respectively, and 90.6% (164/181), 95.6% (65/68), 87.6% (99/113) and 0.92 for the combination of the elastographic and B-mode features, respectively. We conclude that sonoelastographic images and neural network analysis of features has the potential to increase the accuracy of the use of ultrasound for the classification of benign and malignant breast tumors. (E-mail: rfchang@csie.ntu.edu.tw) (C) 2009 World Federation for Ultrasound in Medicine & Biology.This work was supported by a grant from the National Science Council of the Republic of China (NSC96-2221- E-002-268-MY3) and a grant of the Korea Healthcare technology R and D Project, Ministry for Health, Welfare and Family Affairs, Republic of Korea (A070001).Cho N, 2009, EUR RADIOL, V19, P1621, DOI 10.1007/s00330-009-1335-4Muller M, 2009, ULTRASOUND MED BIOL, V35, P219, DOI 10.1016/j.ultrasmedbio.2008.08.018Scaperrotta G, 2008, EUR RADIOL, V18, P2381, DOI 10.1007/s00330-008-1032-8Zhi H, 2008, ACAD RADIOL, V15, P1347, DOI 10.1016/j.acra.2008.08.003Tanter M, 2008, ULTRASOUND MED BIOL, V34, P1373, DOI 10.1016/j.ultrasmedbio.2008.02.002Cho N, 2008, KOREAN J RADIOL, V9, P111, DOI 10.3348/kjr.2008.9.2.111GONZALEZ RC, 2008, DIGITAL IMAGE PROCESBooi RC, 2008, ULTRASOUND MED BIOL, V34, P12, DOI 10.1016/j.ultrasmedbio.2007.07.003Burnside ES, 2007, RADIOLOGY, V245, P401, DOI 10.1148/radiol.2452061805Shen WC, 2007, ULTRASOUND MED BIOL, V33, P1688, DOI 10.1016/j.ultrasmedbio.2007.05.016Shen WC, 2007, ACAD RADIOL, V14, P928, DOI 10.1016/j.acra.2007.04.016Sahiner B, 2007, RADIOLOGY, V242, P716, DOI 10.1148/radiol.2423051464Itoh A, 2006, RADIOLOGY, V239, P341, DOI 10.1148/radiol.2391041676Regner DM, 2006, RADIOLOGY, V238, P425, DOI 10.1148/radiol.2381041336SVENSSON WE, 2006, ULTRASOUND MED BI S1, V32, P173Moon WK, 2005, RADIOLOGY, V236, P458, DOI 10.1148/radiol.2362041095Hong AS, 2005, AM J ROENTGENOL, V184, P1260SVENSSON WE, 2005, P 4 INT C ULTR MEAS, P87Joo S, 2004, IEEE T MED IMAGING, V23, P1292, DOI 10.1109/TMI.2004.834617Horsch K, 2004, ACAD RADIOL, V11, P272, DOI 10.1016/S1076-6332(03)00719-0Chen CM, 2003, RADIOLOGY, V226, P504, DOI 10.1148/radiol.2262011843*AM COLL RAD, 2003, BREAST IM REP DAT SYHall TJ, 2003, ULTRASOUND MED BIOL, V29, P427, DOI 10.1016/S0301-5629(02)00733-0SHAPIRO LG, 2001, COMPUTER VISIONHAYKIN S, 1999, NEURAL NETWORKS COMPGarra BS, 1997, RADIOLOGY, V202, P79OPHIR J, 1991, ULTRASONIC IMAGING, V13, P111