Suzuki Kenji, Li Feng, Sone Shusuke, Doi Kunio
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA.
IEEE Trans Med Imaging. 2005 Sep;24(9):1138-50. doi: 10.1109/TMI.2005.852048.
Low-dose helical computed tomography (LDCT) is being applied as a modality for lung cancer screening. It may be difficult, however, for radiologists to distinguish malignant from benign nodules in LDCT. Our purpose in this study was to develop a computer-aided diagnostic (CAD) scheme for distinction between benign and malignant nodules in LDCT scans by use of a massive training artificial neural network (MTANN). The MTANN is a trainable, highly nonlinear filter based on an artificial neural network. To distinguish malignant nodules from six different types of benign nodules, we developed multiple MTANNs (multi-MTANN) consisting of six expert MTANNs that are arranged in parallel. Each of the MTANNs was trained by use of input CT images and teaching images containing the estimate of the distribution for the "likelihood of being a malignant nodule," i.e., the teaching image for a malignant nodule contains a two-dimensional Gaussian distribution and that for a benign nodule contains zero. Each MTANN was trained independently with ten typical malignant nodules and ten benign nodules from each of the six types. The outputs of the six MTANNs were combined by use of an integration ANN such that the six types of benign nodules could be distinguished from malignant nodules. After training of the integration ANN, our scheme provided a value related to the "likelihood of malignancy" of a nodule, i.e., a higher value indicates a malignant nodule, and a lower value indicates a benign nodule. Our database consisted of 76 primary lung cancers in 73 patients and 413 benign nodules in 342 patients, which were obtained from a lung cancer screening program on 7847 screenees with LDCT for three years in Nagano, Japan. The performance of our scheme for distinction between benign and malignant nodules was evaluated by use of receiver operating characteristic (ROC) analysis. Our scheme achieved an Az (area under the ROC curve) value of 0.882 in a round-robin test. Our scheme correctly identified 100% (76/76) of malignant nodules as malignant, whereas 48% (200/413) of benign nodules were identified correctly as benign. Therefore, our scheme may be useful in assisting radiologists in the diagnosis of lung nodules in LDCT.
低剂量螺旋计算机断层扫描（LDCT）正被用作肺癌筛查的一种方式。然而，放射科医生在LDCT中区分恶性结节和良性结节可能会有困难。我们这项研究的目的是通过使用大规模训练人工神经网络（MTANN）开发一种计算机辅助诊断（CAD）方案，用于区分LDCT扫描中的良性和恶性结节。MTANN是一种基于人工神经网络的可训练、高度非线性滤波器。为了将恶性结节与六种不同类型的良性结节区分开来，我们开发了由六个并行排列的专家MTANN组成的多个MTANN（多MTANN）。每个MTANN通过使用输入CT图像和包含“为恶性结节的可能性”分布估计的教学图像进行训练，即恶性结节的教学图像包含二维高斯分布，而良性结节的教学图像包含零。每个MTANN使用来自六种类型中的每一种的十个典型恶性结节和十个良性结节进行独立训练。六个MTANN的输出通过使用集成人工神经网络进行组合，以便能够将六种类型的良性结节与恶性结节区分开来。在集成人工神经网络训练后，我们的方案提供了一个与结节“恶性可能性”相关的值，即较高的值表示恶性结节，较低的值表示良性结节。我们的数据库由来自日本长野市对7847名受检者进行了三年LDCT肺癌筛查项目的73名患者中的76例原发性肺癌和342名患者中的413个良性结节组成。我们区分良性和恶性结节的方案的性能通过使用受试者操作特征（ROC）分析进行评估。在循环测试中，我们的方案实现了0.882的Az（ROC曲线下面积）值。我们的方案将100%（76/76）的恶性结节正确识别为恶性，而48%（200/413）的良性结节被正确识别为良性。因此，我们的方案可能有助于放射科医生诊断LDCT中的肺结节。
