Comparison of Radiomics Analyses Based on Different Magnetic Resonance Imaging Sequences in Grading and Molecular Genomic Typing of Glioma
Comparison of Radiomics Analyses Based on Different Magnetic Resonance Imaging Sequences in Grading and Molecular Genomic Typing of Glioma
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DOI：
10.1097/rct.0000000000001114
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发表时间：
2021-01-01
影响因子：
1.3
通讯作者：
Zhao, Jian-nong
中科院分区：
文献类型：
--
作者：
Huang, Wei-yuan;Wen, Ling-hua;Zhao, Jian-nong
目的探讨基于不同磁共振序列的放射组学分析在低级别与高级别胶质瘤、异柠檬酸脱氢酶(IDH)1突变与IDH1野生型、突变状态与6-甲基鸟氨酸-DNA甲基转移酶(MGMT)启动子甲基化(+)与MGMT启动子甲基化(-)脑胶质瘤特征的无创性评价中的价值。方法59例未经治疗的脑胶质瘤患者接受了标准的3T-MR肿瘤检查。从MR图像中提取了396个放射组学特征，以手动勾画的肿瘤作为感兴趣的体积。采用单因素Logistic回归分析临床影像诊断特征(肿瘤部位、肿瘤坏死/囊变、是否越过中线、肿瘤强化程度或瘤周水肿)以筛选独立的临床因素。通过多因素Logistic回归和交叉验证，建立了胶质瘤分级和分子基因组分型的放射组学模型和临床-放射组学联合模型。使用接收机工作特性曲线、诺模图和判决曲线对基于不同序列的模型的性能进行了评估。结果基于T1-CE序列的放射组学模型在预测肿瘤分级和IDH1状态方面优于基于其他序列的模型。基于T2的放射组学模型在预测胶质瘤MGMT甲基化状态方面比基于其他序列的模型表现得更好。只有T1联合临床-放射组学模型在预测肿瘤分级和IDH1状态方面表现出更好的预测性能。结论基于多参数MR图像数据和放射组学特征的最新放射组学分析方法对脑胶质瘤的前处理分级和分子亚型分类具有重要意义。
ObjectiveTo investigate the value of radiomics analyses based on different magnetic resonance (MR) sequences in the noninvasive evaluation of glioma characteristics for the differentiation of low-grade glioma versus high-grade glioma, isocitrate dehydrogenase (IDH)1 mutation versus IDH1 wild-type, and mutation status and 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation (+) versus MGMT promoter methylation (-) glioma. MethodsFifty-nine patients with untreated glioma who underwent a standard 3T-MR tumor protocol were included in the study. A total of 396 radiomics features were extracted from the MR images, with the manually delineated tumor as the volume of interest. Clinical imaging diagnostic features (tumor location, necrosis/cyst change, crossing midline, and the degree of enhancement or peritumoral edema) were analyzed by univariate logistic regression to select independent clinical factors. Radiomics and combined clinical-radiomics models were established for grading and molecular genomic typing of glioma by multiple logistic regression and cross-validation. The performance of the models based on different sequences was evaluated by using receiver operating characteristic curves, nomograms, and decision curves. ResultsThe radiomics model based on T1-CE performed better than models based on other sequences in predicting the tumor grade and the IDH1 status of the glioma. The radiomics model based on T2 performed better than models based on other sequences in predicting the MGMT methylation status of glioma. Only the T1 combined clinical-radiomics model showed improved prediction performance in predicting tumor grade and the IDH1 status. ConclusionsThe results demonstrate that state-of-the-art radiomics analysis methods based on multiparametric MR image data and radiomics features can significantly contribute to pretreatment glioma grading and molecular subtype classification.