Computer-aided diagnosis of breast tumors with different US systems.
OBJECTIVE
The authors performed this study to determine whether a computer-aided diagnostic (CAD) system was suitable from one ultrasound (US) unit to another after parameters were adjusted by using intelligent selection algorithms.


METHODS
The authors used texture analysis and data mining with a decision tree model to classify breast tumors with different US systems. The databases of training cases from one unit and testing cases from another were collected from different countries. Regions of interest on US scans and co-variance texture parameters were used in the diagnosis system. Proposed adjustment schemes for different US systems were used to transform the information needed for a differential diagnosis.


RESULTS
Comparison of the diagnostic system with and without adjustment, respectively, yielded the following results: accuracy, 89.9% and 82.2%; sensitivity, 94.6% and 92.2%; specificity, 85.4% and 72.3%; positive predictive value, 86.5% and 76.8%; and negative predictive value, 94.1% and 90.4%. The improvement in accuracy, specificity, and positive predictive value was statistically significant. Diagnostic performance was improved after the adjustment.


CONCLUSIONS
After parameters were adjusted by using intelligent selection algorithms, the performance of the proposed CAD system was better both with the same and with different systems. Different resolutions, different setting conditions, and different scanner ages are no longer obstacles to the application of such a CAD system.
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, Republic of China.
Wen-Jia
WJ
Kuo
Ruey-Feng
RF
Chang
Woo Kyung
WK
Moon
Cheng Chun
CC
Lee
Dar-Ren
DR
Chen
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods