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。
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 p T2, 4 patients in p T3, and 12 patients in p T4. 12 patients(p T4 patients) were classified as serosa positive group, and 12 patients(p T2 and p T3 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 two groups(all P < 0.05). The accuracies of SVM-RFE were 87.5%~94.4%. The output features of SVM-RFE were 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.
百度学术
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