Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression
學年 94
學期 1
出版(發表)日期 2005-08-01
作品名稱 Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression
作品名稱(其他語言)
著者 Woo Kyung Moon; Ruey-Feng Chang; Chii-Jen Chen; Dar-Ren Chen; Wei-Liang Chen
單位
出版者
著錄名稱、卷期、頁數 Radiology 236(2), p.458-464
摘要 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.
關鍵字
語言 en_US
ISSN 0033-8419;1527-1315
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度
國別 USA
公開徵稿
出版型式 ,電子版,紙本