Fogel D B, Wasson E C, Boughton E M, Porto V W, Angeline P J
IEEE Trans Med Imaging. 1998 Jun;17(3):485-8. doi: 10.1109/42.712139.
Computational methods can be used to provide an initial screening or a second opinion in medical settings and may improve the sensitivity and specificity of diagnoses. In the current study, linear discriminant models and artificial neural networks are trained to detect breast cancer in suspicious masses using radiographic features and patient age. Results on 139 suspicious breast masses (79 malignant, 60 benign, biopsy proven) indicate that a significant probability of detecting malignancies can be achieved at the risk of a small percentage of false positives. Receiver operating characteristic (ROC) analysis favors the use of linear models, however, a new measure related to the area under the ROC curve (AZ) suggests a possible benefit from hybridizing linear and nonlinear classifiers.
计算方法可用于在医疗环境中提供初步筛查或第二种意见，并且可能提高诊断的敏感性和特异性。在当前研究中，使用射线照相特征和患者年龄训练线性判别模型和人工神经网络以检测可疑肿块中的乳腺癌。对139个可疑乳腺肿块（79个恶性，60个良性，活检证实）的结果表明，在有一小部分假阳性风险的情况下，可以实现检测恶性肿瘤的显著概率。接受者操作特征（ROC）分析支持使用线性模型，然而，一种与ROC曲线下面积（AZ）相关的新测量方法表明，将线性和非线性分类器混合可能有益。
