[1]陈茂东,张静,杨桂香,等.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[J].南方医科大学学报,2018,(04):428.
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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()
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《南方医科大学学报》[ISSN:/CN:]

卷:
期数:
2018年04期
页码:
428
栏目:
出版日期:
2018-04-30

文章信息/Info

Title:
Differential diagnosis of hepatocellular carcinoma and hepatic hemangiomas based on radiomic features of gadoxetate disodium-enhanced magnetic resonance imaging
作者:
陈茂东张静杨桂香林杰民冯衍秋
关键词:
肝细胞癌肝血管瘤普美显增强磁共振影像组学鉴别诊断
Keywords:
hepatocellular carcinoma hepatic hemangiomas gadoxetate disodium-enhanced magnetic resonance imaging radiomic features differential diagnosis
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。
Abstract:
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 redundancymaximum 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.

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更新日期/Last Update: 1900-01-01