CT理论与应用研究2016，Vol.25Issue(5)：515-522,8.DOI:10.15953/j.1004-4140.2016.25.05.02
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
The Preliminary Study of Spectral CT and Machine Learning Method in Identifying Serosa Invasion of Gastric Cancer
摘要
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.关键词
胃癌/能谱CT/支持向量机回归特征消除Key words
gastric cancer/spectral CT/support vector machine recursive feature elimination分类
信息技术与安全科学引用本文复制引用
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):515-522,8.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）。 （）
