Li Chao, Shi Cen, Zhang Huan, Hui Chun, Lam Kin Man, Zhang Su
Department of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Room 123, No. 1954 Huashan Rd, Xuhui District, Shanghai, China.
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Acad Radiol. 2015 Feb;22(2):149-57. doi: 10.1016/j.acra.2014.08.006. Epub 2014 Sep 22.
This study evaluates the accuracy of dual-energy spectral computed tomography (DEsCT) imaging with the aid of computer-aided diagnosis (CAD) system in assessing serosal invasion in patients with gastric cancer.
Thirty patients with gastric cancer were enrolled in this study. Two types of features (information) were collected with the use of DEsCT imaging: conventional features including patient's clinical information (eg, age, gender) and descriptive characteristics on the CT images (eg, location of the lesion, wall thickness at the gastric cardia) and additional spectral CT features extracted from monochromatic images (eg, 60 keV) and material-decomposition images (eg, iodine- and water-density images). The classification results of the CAD system were compared to pathologic findings. Important features can be found out using support vector machine classification method in combination with feature-selection technique thereby helping the radiologists diagnose better.
Statistical analysis showed that for the collected cases, the feature "long axis" was significantly different between group A (serosa negative) and group B (serosa positive) (P < .05). By adding quantitative spectral features from several regions of interest (ROIs), the total classification accuracy was improved from 83.33% to 90.00%. Two feature ranking algorithms were used in the CAD scheme to derive the top-ranked features. The results demonstrated that low single-energy (approximately 60 keV) CT values, tumor size (long axis and short axis), iodine (water) density, and Effective-Z values of ROIs were important for classification. These findings concurred with the experience of the radiologist.
The CAD system designed using machine-learning algorithms may be used to improve the identification accuracy in the assessment of serosal invasion in patients of gastric cancer with DEsCT imaging and provide some indicators which may be useful in predicting prognosis.
本研究评估在计算机辅助诊断（CAD）系统辅助下的双能谱计算机断层扫描（DEsCT）成像在评估胃癌患者浆膜侵犯方面的准确性。
本研究纳入了30例胃癌患者。使用DEsCT成像收集了两种类型的特征（信息）：常规特征，包括患者的临床信息（如年龄、性别）和CT图像上的描述性特征（如病变位置、贲门处胃壁厚度），以及从单色图像（如60keV）和物质分解图像（如碘密度和水密度图像）中提取的额外光谱CT特征。将CAD系统的分类结果与病理结果进行比较。使用支持向量机分类方法结合特征选择技术可以找出重要特征，从而帮助放射科医生更好地进行诊断。
统计分析表明，对于所收集的病例，A组（浆膜阴性）和B组（浆膜阳性）之间的“长轴”特征存在显著差异（P <.05）。通过添加来自多个感兴趣区域（ROI）的定量光谱特征，总分类准确率从83.33%提高到了90.00%。在CAD方案中使用了两种特征排序算法来得出排名靠前的特征。结果表明，低单能（约60keV）CT值、肿瘤大小（长轴和短轴）、碘（水）密度以及ROI的有效原子序数（Effective-Z值）对于分类很重要。这些发现与放射科医生的经验一致。
使用机器学习算法设计的CAD系统可用于提高DEsCT成像评估胃癌患者浆膜侵犯的识别准确率，并提供一些可能有助于预测预后的指标。
