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Computer Applications |
1 From the Department of General Surgery, China Medical College and Hospital, 2 Yer-Der Rd, Taichung, Taiwan, Republic of China (D.R.C.), and the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, Republic of China (R.F.C., Y.L.H.). Received May 16, 1998; revision requested July 30; final revision received April 22, 1999; accepted June 8. Address reprint requests to D.R.C. (e-mail: dlchen88@ms13.hinet.net).
PURPOSE: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network.
MATERIALS AND METHODS: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an analog video signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves.
RESULTS: The accuracy of neural networks for classifying malignancies was 95.0% (133 of 140 tumors), the sensitivity was 98% (51 of 52), the specificity was 93% (82 of 88), the positive predictive value was 89% (51 of 57), and the negative predictive value was 99% (82 of 83).
CONCLUSION: This system differentiated solid breast nodules with relatively high accuracy and helped inexperienced operators to avoid misdiagnoses. Because the neural network is trainable, it could be optimized if a larger set of tumor images is supplied.
Index terms: Breast neoplasms, diagnosis, 00.30 • Breast neoplasms, US, 00.1298 • Computers, diagnostic aid • Images, analysis • Images, interpretation • Ultrasound (US), tissue characterization, 00.1298
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