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

Woo Kyung Moon, Ruey Feng Chang, Chii Jen Chen, Dar Ren Chen, Wei Liang Chen

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

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 (AZ) values for contour difference, shift distance, area difference, and solidity were 0.88, 0.85, 0.86, and 0.79, respectively. The AZ 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 AZ 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.

Original languageEnglish
Pages (from-to)458-464
Number of pages7
JournalRadiology
Volume236
Issue number2
DOIs
StatePublished - 1 Aug 2005

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