胰腺影像组学对乙肝肝硬化患者肝源性糖尿病的预测价值  

The value of pancreatic imaging in predicting diabetes in patients with Hepatitis B cirrhosis

作　　者：王司琪 郭东强 武志峰 WANG Siqi;GUO Dongqiang;WU Zhifeng(Shanxi Medical University,Taiyuan 030001,China;Shanxi Bethune Hospital,Shanxi Academy of Medical Sciences,Taiyuan 030032,China)

机构地区：[1]山西医科大学医学影像学院,山西太原030001 [2]山西白求恩医院,山西医学科学院,山西太原030032

出　　处：《临床医药实践》2024年第7期527-534,共8页Proceeding of Clinical Medicine

摘　　要：目的:分析乙肝肝硬化患者胰腺CT平扫及双能量CT(DECT)增强扫描动静脉期影像组学特征,及其对肝源性糖尿病(HD)的预测价值。方法:回顾性分析乙肝肝硬化患者胰腺CT平扫及DECT增强扫描动静脉期图像资料,采用慧影科研平台“慧医慧影”提取影像组学特征,筛选出最优特征,收集患者肝功能分级(Child-Pugh分级)等多项临床数据,采用高斯朴素贝叶斯(Gaussian NB)、被动攻击型算法(PA)和支持向量机(SVM)三种机器学习分类算法,构建仅基于影像组学特征的模型(影像组学模型)和基于影像组学特征联合临床变量预测模型(联合模型),预测患者是否合并HD。采用受试者工作特征曲线下面积(AUC)等比较各算法构建的影像组学模型和联合模型的预测诊断效能。结果:肝炎肝硬化患者141例,经纳入和排除标准筛选后,共计134例患者纳入研究。依据临床诊断是否合并糖尿病分为HD组(49例)和nHD组(85例)。两组Child-Pugh分级比较,差异有统计学意义(P<0.05)。两组影像组学特征比较,差异有统计学意义(P<0.05)。Gaussian NB,PA和SVM联合模型对训练组预测诊断AUC值分别为0.919,0.821和0.845。将134例患者数据按照7∶3的比例随机分为训练组(94例)和测试组(40例),分别构建基于影像组学特征、基于影像组学特征和临床变量(联合模型)。测试组的Gaussian NB,PA和SVM预测诊断AUC值分别为0.579,0.614和0.670。经Delong检验显示,三种机器学习分类算法构建的联合模型效能比较,差异无统计学意义(P>0.05)。与联合模型相比,影像组学模型预测效能较低。结论:基于CT平扫及DECT增强扫描动静脉期影像组学特征,联合肝功能分级等多项临床数据构建的机器学习网络模型,对乙肝肝硬化患者是否合并HD有一定的预测价值。Objective:This study aims to analyze the imaging characteristics of pancreatic CT plain scans and dual-energy CT(DECT)enhanced scans in patients with hepatitis B cirrhosis.Additionally,it seeks to evaluate the efficacy of machine learning approaches in predicting the comorbidity of hepatogenic diabetes mellitus(HD)in hepatitis B cirrhosis patients.Methods:Clinical data,including Child-Pugh grading,and images from pancreatic CT plain scans and DECT enhanced scans were collected from patients with hepatitis B cirrhosis.The imaging features were extracted using the scientific research platform and the software of“Huiyi Huiying”.Subsequently,the optimal imaging features were processed by the machine learning approach,Gaussian naive Bayesian(Gaussian NB),support vector machine(SVM)and passive aggressive algorithm(PA),respectively,to predict the presence of HD in patients with hepatitis B cirrhosis.Lastly,we compared the predictive diagnostic efficacy under three conditions:using only CT imaging data;combining CT imaging and clinical features;applying three machine learning approaches.Results:In 141 patients with hepatitis and cirrhosis,134 patients were included in this study after screening for inclusion and exclusion criteria.They were divided into HD Group(n=49)and nHD group(n=85).There was significant difference in Child-Pugh grading between the two groups(P<0.05).When combining CT imaging and clinical features,the predictive diagnostic efficacy,as indicated by the AUC values,for Gaussian NB,PA and SVM in the training group were 0.919,0.821 and 0.845,respectively.The data of 134 patients were randomly divided into the training group(n=94)and the test group(n=40)in a 7∶3 ratio,based on imaging features,histological features and clinical variables,respectively(Combined Model).The predictive diagnostic AUC of Gaussian NB,PA and SVM in test group was 0.579,0.614 and 0.670 respectively.Delong test showed that there was no significant difference in the efficiency of the joint model constructed by the three machine

关 键 词：胰腺 肝硬化 影像组学 双能量CT 

分 类 号：R445[医药卫生—影像医学与核医学]