Chang Ruey-Feng, Wu Wen-Jie, Moon Woo Kyung, Chou Yi-Hong, Chen Dar-Ren
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan.
Acad Radiol. 2003 Feb;10(2):189-97. doi: 10.1016/s1076-6332(03)80044-2.
Breast cancer has become the leading cause of cancer deaths among women in developed countries. To decrease the related mortality, disease must be treated as early as possible, but it is hard to detect and diagnose tumors at an early stage. A well-designed computer-aided diagnostic system can help physicians avoid misdiagnosis and avoid unnecessary biopsy without missing cancers. In this study, the authors tested one such system to determine its effectiveness.
Many computer-aided diagnostic systems for ultrasonography are based on the neural network model and classify breast tumors according to texture features. The authors tested a refinement of this model, an advanced support vector machine (SVM), in 250 cases of pathologically proved breast tumors (140 benign and 110 malignant), and compared its performance with that of a multilayer propagation neural network.
The accuracy of the SVM for classifying malignancies was 85.6% (214 of 250); the sensitivity, 95.45% (105 of 110); the specificity, 77.86% (109 of 140); the positive predictive value, 77.21% (105 of 136); and the negative predictive value, 95.61% (109 of 114).
The SVM proved helpful in the imaging diagnosis of breast cancer. The classification ability of the SVM is nearly equal to that of the neural network model, and the SVM has a much shorter training time (1 vs 189 seconds). Given the increasing size and complexity of data sets, the SVM is therefore preferable for computer-aided diagnosis.
在发达国家，乳腺癌已成为女性癌症死亡的主要原因。为降低相关死亡率，疾病必须尽早治疗，但早期肿瘤难以检测和诊断。一个设计良好的计算机辅助诊断系统可以帮助医生避免误诊，避免不必要的活检，同时不漏诊癌症。在本研究中，作者测试了这样一个系统以确定其有效性。
许多超声计算机辅助诊断系统基于神经网络模型，并根据纹理特征对乳腺肿瘤进行分类。作者在250例经病理证实的乳腺肿瘤（140例良性和110例恶性）中测试了该模型的改进版——高级支持向量机（SVM），并将其性能与多层传播神经网络的性能进行比较。
SVM对恶性肿瘤分类的准确率为85.6%（250例中的214例）；灵敏度为95.45%（110例中的105例）；特异度为77.86%（140例中的109例）；阳性预测值为77.21%（136例中的105例）；阴性预测值为95.61%（114例中的109例）。
SVM在乳腺癌的影像诊断中被证明是有帮助的。SVM的分类能力与神经网络模型几乎相当，且SVM的训练时间短得多（1秒对189秒）。鉴于数据集规模和复杂性不断增加，因此SVM更适合用于计算机辅助诊断。
