Nakamura K, Yoshida H, Engelmann R, MacMahon H, Katsuragawa S, Ishida T, Ashizawa K, Doi K
Department of Radiology, University of Chicago, IL 60637, USA.
Radiology. 2000 Mar;214(3):823-30. doi: 10.1148/radiology.214.3.r00mr22823.
To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) to assist radiologists in the distinction of benign and malignant pulmonary nodules.
Fifty-six chest radiographs of 34 primary lung cancers and 22 benign nodules were digitized with a 0.175-mm pixel size and a 10-bit gray scale. Eight subjective image features were evaluated and recorded by radiologists in each case. A computerized method was developed to extract objective features that could be correlated with the subjective features. An ANN was used to distinguish benign from malignant nodules on the basis of subjective or objective features. The performance of the ANN was compared with that of the radiologists by means of receiver operating characteristic (ROC) analysis.
Performance of the ANN was considerably greater with objective features (area under the ROC curve, Az = 0.854) than with subjective features (Az = 0.761). Performance of the ANN was also greater than that of the radiologists (Az = 0.752).
The computerized scheme has the potential to improve the diagnostic accuracy of radiologists in the distinction of benign and malignant solitary pulmonary nodules.
通过使用人工神经网络（ANN）开发一种计算机辅助诊断方案，以协助放射科医生鉴别良性和恶性肺结节。
对34例原发性肺癌和22例良性结节的56张胸部X光片进行数字化处理，像素大小为0.175毫米，灰度为10位。放射科医生对每个病例评估并记录8个主观图像特征。开发了一种计算机化方法来提取与主观特征相关的客观特征。基于主观或客观特征，使用人工神经网络区分良性和恶性结节。通过接受者操作特征（ROC）分析将人工神经网络的性能与放射科医生的性能进行比较。
人工神经网络基于客观特征的性能（ROC曲线下面积，Az = 0.854）比基于主观特征的性能（Az = 0.761）要好得多。人工神经网络的性能也优于放射科医生（Az = 0.752）。
该计算机化方案有可能提高放射科医生鉴别良性和恶性孤立性肺结节的诊断准确性。
