Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression

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Moon, Woo Kyung; Chang, Ruey-Feng; Chen, Chii-Jen; Chen, Dar-Ren; Chen, Wei-Liang
Issue Date
Radiological Society of North America
Radiology 2005; 236:458–464.
PURPOSE: To prospectively evaluate the accuracy of continuous ultrasonographic (US) images obtained during probe compression and computer-aided analysis for classification of biopsy-proved (reference standard) benign and malignant breast tumors. MATERIALS AND METHODS: This study was approved by the local ethics committee, and informed consent was obtained from all included patients. Serial US images of 100 solid breast masses (60 benign and 40 malignant tumors) were obtained with US probe compression in 86 patients (mean age, 45 years; range, 20-67 years). After segmentation of tumor contours with the level-set method, three features of strain on tissue from probe compression--contour difference, shift distance, area difference--and one feature of shape--solidity-were computed. A maximum margin classifier was used to classify the tumors by using these four features. The Student t test and receiver operating characteristic curve analysis were used for statistical analysis. RESULTS: The mean values of contour difference, shift distance, area difference, and solidity were 3.52% +/- 2.12 (standard deviation), 2.62 +/- 1.31, 1.08% +/- 0.85, and 1.70 +/- 1.85 in malignant tumors and 9.72% +/- 4.54, 5.04 +/- 2.79, 3.17% +/- 2.86, and 0.53 +/- 0.63 in benign tumors, respectively. Differences with P < .001 were statistically significant for all four features. Area under the receiver operating characteristic curve (A(Z)) values for contour difference, shift distance, area difference, and solidity were 0.88, 0.85, 0.86, and 0.79, respectively. The A(Z) value of three features of strain was significantly higher than that of the feature of shape (P < .01). The accuracy, sensitivity, specificity, and positive and negative predictive values of US classifications that were based on values for these four features were 87.0% (87 of 100), 85% (34 of 40), 88% (53 of 60), 83% (34 of 41), and 90% (53 of 59), respectively, with an A(Z) value of 0.91. CONCLUSION: Continuous US images obtained with probe compression and computer-aided analysis can aid in classification of benign and malignant breast tumors.
0033-8419 (Print)
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College of Medicine/School of Medicine (의과대학/대학원)Radiology (영상의학전공)Journal Papers (저널논문_영상의학전공)
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