Aoyama Masahito, Li Qiang, Katsuragawa Shigehiko, Li Feng, Sone Shusuke, Doi Kunio
Kurt Rossmann Laboratory for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Avenue, Chicago, Illinois 60637, USA.
Med Phys. 2003 Mar;30(3):387-94. doi: 10.1118/1.1543575.
An automated computerized scheme has been developed for determination of the likelihood measure of malignancy of pulmonary nodules on low-dose helical CT (LDCT) images. Our database consisted of 76 primary lung cancers (147 slices) and 413 benign nodules (576 slices). With this automated computerized scheme, the location of a nodule was first indicated by a radiologist. The outline of the nodule was segmented automatically by use of a dynamic programming technique. Various objective features on the nodules were determined by use of outline analysis and image analysis, and the likelihood measure of malignancy was determined by use of linear discriminant analysis (LDA). The effect of many different combinations of features and the performance of LDA in distinguishing benign nodules from malignant ones were evaluated by means of receiver operating characteristic (ROC) analysis. The Az value (area under the ROC curve) obtained by the computerized scheme in distinguishing benign nodules from malignant ones was 0.828 when a single slice was employed for each of the nodules. However, the Az value was improved to 0.846 when multiple slices were used for determination of the likelihood measure of malignancy. The Az values obtained by the computerized scheme on LDCT images were significantly greater than the Az value of 0.70, which was obtained from our previous observer studies by radiologists in distinguishing benign nodules from malignant ones on LDCT images. The automated computerized scheme for determination of the likelihood measure of malignancy would be useful in assisting radiologists to distinguish between benign and malignant pulmonary nodules on LDCT images.
已经开发出一种自动化的计算机方案，用于确定低剂量螺旋CT（LDCT）图像上肺结节恶性可能性的测量值。我们的数据库由76例原发性肺癌（147个切片）和413个良性结节（576个切片）组成。利用这种自动化的计算机方案，首先由放射科医生指出结节的位置。通过使用动态规划技术自动分割结节的轮廓。通过轮廓分析和图像分析确定结节的各种客观特征，并使用线性判别分析（LDA）确定恶性可能性的测量值。通过接受者操作特征（ROC）分析评估了许多不同特征组合的效果以及LDA区分良性结节和恶性结节的性能。当每个结节采用单个切片时，计算机方案在区分良性结节和恶性结节时获得的Az值（ROC曲线下面积）为0.828。然而，当使用多个切片来确定恶性可能性的测量值时，Az值提高到了0.846。计算机方案在LDCT图像上获得的Az值显著大于0.70，后者是我们之前放射科医生在LDCT图像上区分良性结节和恶性结节的观察者研究中获得的。用于确定恶性可能性测量值的自动化计算机方案将有助于放射科医生在LDCT图像上区分良性和恶性肺结节。
