Breast Ultrasound Computer-Aided Diagnosis Using BI-RADS Features

Shen, Wei-Chih; Chang, Ruey-Feng; Moon, Woo Kyung; Chou, Yi-Hong; Huang, Chiun-Sheng

doi:10.1016/j.acra.2007.04.016

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Volume 14, Issue 8 , Pages 928-939, August 2007

Breast Ultrasound Computer-Aided Diagnosis Using BI-RADS Features

Wei-Chih Shen, MS
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan 621, R.O.C.
Ruey-Feng Chang, PhD
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan 621, R.O.C.
- Department of Computer Science and Information Engineering, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan 10617, R.O.C.
- Address correspondence to R.-F.C.:
Woo Kyung Moon, MD
- Department of Radiology and Clinical Research Institute, Seoul National University Hospital, Seoul, Korea
Yi-Hong Chou, MD
- Department of Radiology, Taipei Veterans General Hospital, National Yang Ming University School of Medicine, Taipei, Taiwan
Chiun-Sheng Huang, MD
- Department of Surgery, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan.

Received 17 February 2007; accepted 21 April 2007.

Rationale and Objectives

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.

Materials and 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%.

Conclusion

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.

Key Words: Breast cancer, BI-RADS, ultrasound, computer-aided diagnosis (CAD) system, binary logistic regression model