Breast ultrasound computer-aided diagnosis using BI-RADS features.
OBJECTIVE
Based on the definitions in mass category of Breast Imaging Reporting and Data System developed by American College of Radiology, eight computerized features including shape, orientation, margin, lesion boundary, echo pattern, and posterior acoustic feature classes are proposed.


METHODS
Our experimental database consists of 265 pathology-proven cases including 180 benign and 85 malignant masses. The capacity of each proposed feature in differentiating malignant from benign masses was validated by Student's t test and the correlation between each proposed feature and the pathological result was evaluated by point biserial coefficient. Binary logistic regression model was used to relate all proposed features and pathological result as a computer-aided diagnosis (CAD) system. The diagnostic value of each proposed feature in the CAD system was further evaluated by the feature selection methods. Additionally, the likelihood of malignancy for each individual feature was also estimated by binary logistic regression.


RESULTS
On each proposed feature, the malignant cases were significantly different from the benign ones. The correlation between the angular characteristic and pathological result was indicated as very high. Three substantial correlations appear in features irregular shape, undulation characteristic, and degree of abrupt interface, but the relationship for orientation feature is low. For the constructed CAD system, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 91.70% (243 of 265), 90.59% (77 of 85), 92.22% (166 of 180), 84.62% (77 of 91), and 95.40% (166 of 174), respectively, and the area index in the ROC analysis was 0.97. Compared with the significant contribution of angular characteristic, the diagnostic values of posterior acoustic feature and orientation feature were relatively low for the CAD system. When three or more angular characteristics are discovered or the degree of abrupt interface is lower than 18, the likelihood of malignancy could be predicted as greater than 40%.


CONCLUSIONS
The computerized BI-RADS sonographic features conform to the sign of malignancy in the clinical experience and efficiently help the CAD system to diagnose the mass.
Shen
Wei-Chih
WC
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, ROC.
Chang
Ruey-Feng
RF
Moon
Woo Kyung
WK
Chou
Yi-Hong
YH
Huang
Chiun-Sheng
CS
D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary