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Ruey-Feng Chang, PhD; Wen-Jia Kuo, MS; Dar-Ren Chen, MD; Yu-Len Huang, PhD; Jau-Hong Lee, MD; Yi-Hong Chou, MD

Arch Surg. 2000;135:696-699.

Hypothesis  The computer-aided diagnostic system is an intelligent system with great potential for categorizing solid breast nodules. It can be used conveniently for surgical office-based digital ultrasonography (US) of the breast.

Design  Retrospective, nonrandomized study.

Setting  University teaching hospital.

Patients  We retrospectively reviewed 243 medical records of digital US images of the breast of pathologically proved, benign breast tumors from 161 patients (ie, 136 fibroadenomas and 25 fibrocystic nodules), and carcinomas from 82 patients (ie, 73 invasive duct carcinomas, 5 invasive lobular carcinomas, and 4 intraductal carcinomas). The digital US images were consecutively recorded from January 1, 1997, to December 31, 1998.

Intervention  The physician selected the region of interest on the digital US image. Then a learning vector quantization model with 24 autocorrelation texture features is used to classify the tumor as benign or malignant. In the experiment, 153 cases were arbitrarily selected to be the training set of the learning vector quantization model and 90 cases were selected to evaluate the performance. One experienced radiologist who was completely blind to these cases was asked to classify these tumors in the test set.

Main Outcome Measure  Contribution of breast US to diagnosis.

Results  The performance comparison results illustrated the following: accuracy, 90%; sensitivity, 96.67%; specificity, 86.67%; positive predictive value, 78.38%; and negative predictive value, 98.11% for the computer-aided diagnostic (CAD) system and accuracy, 86.67%; sensitivity, 86.67%; specificity, 86.67%; positive predictive value, 76.47%; and negative predictive value, 92.86% for the radiologist.

Conclusion  The proposed CAD system provides an immediate second opinion. An accurate preoperative diagnosis can be routinely established for surgical office-based digital US of the breast. The diagnostic rate was even better than the results of an experienced radiologist. The high negative predictive rate by the CAD system can avert benign biopsies. It can be easily implemented on exisiting commercial diagnostic digital US machines. For most available diagnostic digital US machines, all that would be required for the CAD system is only a personal computer loaded with CAD software.

From the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan (Drs Chang, and Huang and Mr Kuo); Department of General Surgery, China Medical College and Hospital, Taichung, Taiwan (Drs Chen and Lee); and the Department of Radiology, Veterans General Hospital, Taipei, Taiwan (Dr Chou).

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