| 宝石能谱CT及机器学习算法在判断胃癌浆膜浸润中的初步应用 |
| |
| 引用本文： | 诗涔,张欢,潘自来,严福华,李超,张素,杜联军.宝石能谱CT及机器学习算法在判断胃癌浆膜浸润中的初步应用[J].CT理论与应用研究,2016,25(5):515-522. |
| |
| 作者姓名： | 诗涔 张欢 潘自来 严福华 李超 张素 杜联军 |
| |
| 作者单位： | 1. 上海交通大学医学院附属瑞金医院放射科, 上海 200025; |
| |
| 基金项目： | 上海科委医学引导项目(134119a5900)；国家自然基金(U1532107；81272746)；上海交通大学医工交叉基金(YG2014MS53)。 |
| |
| 摘 要： | 目的:探讨宝石能谱CT及机器学习算法在判断胃癌浆膜浸润中的价值。方法:回顾性分析在我院行宝石能谱CT双期GSI增强检查的胃癌患者24例,其中p T2 8例,p T3 4例,p T4 12例。12例患者(p T4)归为浆膜阳性组(组A);12例(T2和T3)归为浆膜阴性组(组B)。采用独立样本t检验或卡方检验比较两组患者的临床信息(如性别、年龄等)。此外,所有图像利用GE AW4.4工作站进行后处理,分别得出两组病灶双期能谱信息,随后采用SVM-RFE算法对两组能谱信息进行分析。结果:两组患者的临床信息中,肿瘤长径和短径在两组间有统计学差异(P均<0.05)。SVM-RFE算法的准确率为87.5%-94.4%。SVM-RFE的输出结果为门脉期脂肪(钙)、门脉期尿酸(钙)、动脉期钙(碘)、门脉期水(钙)、门脉期碘(水)。结论:肿瘤大小和门脉期脂肪(钙)、门脉期尿酸(钙)、动脉期钙(碘)、门脉期水(钙)及门脉期碘(水)特征值可用于辅助判定胃癌是否浸润浆膜层。
|
| 关 键 词： | 胃癌 能谱CT 支持向量机回归特征消除 |
| 收稿时间： | 2016-04-20 |
The Preliminary Study of Spectral CT and Machine Learning Method in Identifying Serosa Invasion of Gastric Cancer |
| |
| SHI Cen,ZHANG Huan,PANZi-lai,YAN Fu-hua,LI Chao,ZHANG Su,DU Lian-jun.The Preliminary Study of Spectral CT and Machine Learning Method in Identifying Serosa Invasion of Gastric Cancer[J].Computerized Tomography Theory and Applications,2016,25(5):515-522. |
| |
| Authors: | SHI Cen ZHANG Huan PANZi-lai YAN Fu-hua LI Chao ZHANG Su DU Lian-jun |
| |
| Affiliation: | 1. Department of Radiology, Ruijin Hospital affiliated to Shanghai Jiao Tong University Medical School, Shanghai 200025, China;2. Department of Radiology, the First Affiliated Hospital of Soochow University, Jiangsu Suzhou 215006, China;3. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China |
| |
| Abstract: | Objective: To evaluate the value of spectral CT and machine learning method in identifying serosa invasion of gastric cancer. Method: Total of 24 cases of gastric cancer who underwent dual-phasic scans (arterial phase (AP) and portal phase (PP)) with GSI mode on high-definition computed tomography were retrospectively enrolled in our study, including 8 patients in pT2, 4 patients in pT3, and 12 patients in pT4. 12 patients (pT4 patients) were classified as serosa positive group, and 12 patients (pT2 and pT3 patients) were classified as serosa negative group. The clinical information (e.g.sex, age) of these two groups were compared by using independent sample t test or chi square test. In addition, GE AW4.4 workstation was used for image post-processing, and the dual phase spectrum information of these two groups was obtained. Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm was used to analyze the spectrum information of these two groups. Results:Among the clinical information, only tumor long axis and short axis had statistically significant difference between twogroups (allP<0.05). The accuracies of SVM-RFE were 87.5%~94.4%. The output featuresof SVM-RFEwere fat(calcium)(PP), uricacid(calcium)(PP), calcium(iodine)(AP), water(calcium)(PP), and iodine(water)(PP). Conclusion: Tumor size, fat(calcium)(PP), uricacid(calcium)(PP), calcium(iodine)(AP), water(calcium)(PP), and iodine(water)(PP)were helpful for the diagnosis of gastric cancer serosa invasion. |
| |
| Keywords: | gastric cancer spectral CT support vector machine recursive feature elimination |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《CT理论与应用研究》浏览原始摘要信息 |
|
点击此处可从《CT理论与应用研究》下载全文 |
|
