Breast cancer diagnosis using self-organizing map for sonography

The purpose of this study was to evaluate the performance of neural network model self-organizing maps (SOM) in the classification of benign and malig nant sonographic breast lesions. A total of 243 breast tumors (82 malignant and 161 benign) were retrospectively evaluated. When a sonogram was perfor med, the analog video signal was captured to obtain a digitized sonographic image. The physician selected the region of interest in the sonography, An SOM model using 24 autocorrelation texture features classified the tumor a s benign or malignant. In the experiment, cases were sampled with k-fold cr oss-validation (k = 10) to evaluate the performance using receiver operatin g characteristic (ROC) curves,The ROC area index for the proposed SOM syste m is 0.9357 +/- 0.0152, the accuracy is 85.6%, the sensitivity is 97.6%, th e specificity is 79.5%, the positive predictive value is 70.8%, and the neg ative predictive value is 98.5%. This computer-aided diagnosis system can p rovide a useful tool and its high negative predictive value could potential ly help avert benign biopsies. (C) 2000 World Federation for Ultrasound in Medicine & Biology.