基于普美显增强磁共振的影像组学鉴别肝细胞癌与肝血管瘤

摘要

目的 基于普美显增强磁共振图像,评估影像组学方法在鉴别肝细胞癌(HCC)与肝血管瘤(HHE)的可行性.方法 收集HCC病人与HHE病人的普美显增强磁共振数据(总共135个病灶),在肝特异期图像勾画病灶,利用影像组学方法提取每个病灶的纹理特征.单特征分析:用两样本t检验或Mann Whitney U检验、ROC分析评估每个特征对HCC和HHE的区分程度及分类性能;多特征分析:首先比较3种特征选择算法(最小冗余-最大相关、近邻成分分析、序列前向选择)的性能,根据最优的特征选择算法确定最优特征子集,最后将特征选择的结果在3种分类器算法(支持向量机、线性判别分析、逻辑回归)上进行训练与测试,整个分析过程均采用重复5次10折交叉验证实验.结果 对于单特征分析,超过50%的特征具有较强的区分能力,其中灰度共生矩阵特征S(3,-3)SumEntrp的分类性能:AUC为0.72(P<0.01),敏感性为0.83,特异性为0.57;对于多特征分析,特征选择算法比较的结果为序列前向搜索算法更优;最终基于该算法选择15个特征,其中支持向量机分类器上得到的平均分类性能:测试准确率:0.82±0.09,AUC为0.86±0.12,敏感性为0.88±0.11,特异性为0.76±0.18.结论 基于普美显增强磁共振图像,使用影像组学方法能够很好地鉴别肝细胞癌与肝血管瘤,为临床辅助诊断提供有利的手段.%Objective To evaluate the feasibility of using radiomic features for differential diagnosis of hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HHE). Methods Gadoxetate disodium-enhanced magnetic resonance imaging data were collected from a total of 135 HCC and HHE lesions.The radiomic texture features of each lesion were extracted on the hepatobiliary phase images,and the performance of each feature was assessed in differentiation and classification of HCC and HHE. In multivariate analysis, the performance of 3 feature selection algorithms (namely minimum redundancy-maximum relevance, mRmR; neighborhood component analysis, NCA; and sequence forward selection, SFS) was compared. The optimal feature subset was determined according to the optimal feature selection algorithm and used for testing the 3 classifier algorithms (namely the support vector machine, RBF-SVM; linear discriminant analysis, LDA; and logistic regression). All the tests were repeated 5 times with 10-fold cross validation experiments. Results More than 50% of the radiomic features exhibited strong distinguishing ability, among which gray level co-occurrence matrix feature S (3,-3) SumEntrp showed a good classification performance with an AUC of 0.72(P<0.01),a sensitivity of 0.83 and a specificity of 0.57. For the multivariate analysis, 15 features were selected based on the SFS algorithm, which produced better results than the other two algorithms. Testing of these 15 selected features for their average cross-validation performance with RBF-SVM classifier yielded a test accuracy of 0.82±0.09, an AUC of 0.86±0.12, a sensitivity of 0.88±0.11, and a specificity of 0.76±0.18. Conclusion The radiomic features based on gadoxetate disodium-enhanced magnetic resonance images allow efficient differential diagnosis of HCC and HHE, and can potentially provide important assistance in clinical diagnosis of the two diseases.