Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Comput Methods Programs Biomed. 2017 Jul;145:45-51. doi: 10.1016/j.cmpb.2017.04.008. Epub 2017 Apr 13.
Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images.
A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation.
The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713.
Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system.
肝癌是美国第十大常见癌症，其发病率几十年来一直在上升。早期发现、诊断和治疗这种疾病非常重要。计算机断层扫描（CT）是检测肝癌最常用和最强大的成像技术之一。CT 扫描仪可以提供整个肝脏的多期序贯扫描。在本研究中，我们提出了一种计算机辅助诊断（CAD）系统，该系统使用多期 CT 图像中获得的肿瘤特征来诊断肝癌。
共评估了 71 个经组织学证实的肝肿瘤，包括 49 个良性肿瘤和 22 个恶性肿瘤，使用所提出的 CAD 系统来评估其性能。用户识别肿瘤，然后使用区域生长算法对其进行分割。肿瘤分割后，为每个肿瘤获取三种类型的特征，包括纹理、形状和动力学曲线。纹理使用基于灰度共生矩阵（GLCM）的肿瘤三维（3D）纹理数据进行量化。紧凑性、边缘和椭圆模型用于描述肿瘤的 3D 形状。动力学曲线是从每个肿瘤的各个阶段建立的，表现为各阶段之间密度的变化。采用向后消元法选择特征的最佳组合，并采用二项逻辑回归分析进行留一交叉验证的肿瘤分类。
纹理的准确率和敏感度分别为 71.82%和 68.18%，在封闭特异性下优于形状和动力学曲线。结合所有特征可获得最高的准确率（58/71，81.69%）、敏感度（18/22，81.82%）和特异性（40/49，81.63%）。结合所有特征的 Az 值为 0.8713。
使用我们提出的 CAD 系统，结合纹理、形状和动力学曲线特征，可能能够区分肝脏中的良性和恶性肿瘤。
