Chen D, Chang R F, Huang Y L
Department of General Surgery, China Medical College and Hospital, Taichung, Taiwan.
Ultrasound Med Biol. 2000 Mar;26(3):405-11. doi: 10.1016/s0301-5629(99)00156-8.
The purpose of this study was to evaluate the performance of neural network model self-organizing maps (SOM) in the classification of benign and malignant sonographic breast lesions. A total of 243 breast tumors (82 malignant and 161 benign) were retrospectively evaluated. When a sonogram was performed, 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 as benign or malignant. In the experiment, cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance using receiver operating characteristic (ROC) curves. The ROC area index for the proposed SOM system is 0.9357 +/- 0.0152, the accuracy is 85. 6%, the sensitivity is 97.6%, the specificity is 79.5%, the positive predictive value is 70.8%, and the negative predictive value is 98. 5%. This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies.
本研究的目的是评估神经网络模型自组织映射（SOM）在乳腺超声良恶性病变分类中的性能。回顾性评估了总共243例乳腺肿瘤（82例恶性和161例良性）。进行超声检查时，采集模拟视频信号以获得数字化超声图像。医生在超声检查中选择感兴趣区域。使用24个自相关纹理特征的SOM模型将肿瘤分类为良性或恶性。在实验中，采用k折交叉验证（k = 10）对病例进行采样，以使用受试者操作特征（ROC）曲线评估性能。所提出的SOM系统的ROC面积指数为0.9357 +/- 0.0152，准确率为85.6%，灵敏度为97.6%，特异性为79.5%，阳性预测值为70.8%，阴性预测值为98.5%。这种计算机辅助诊断系统可以提供一个有用的工具，其高阴性预测值可能有助于避免良性活检。
