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神经突方向离散度和密度成像预测成人型弥漫性胶质瘤IDH基因型的应用OA北大核心CSTPCD

Application of neurite orientation dispersion and density imaging to predict IDH genotype of adult diffuse glioma

中文摘要英文摘要

目的 运用神经突方向离散度与密度成像(neurite orientation dispersion and density imaging,NODDI)定量参数评估不同异柠檬酸脱氢酶(isocitrate dehydrogenase,IDH)基因状态肿瘤的差异性及探讨NODDI在成人型弥漫性胶质瘤(adult-type diffuse glioma,ADG)基因分型上的临床应用价值.材料与方法 回顾性分析了51例(IDH突变型21例,IDH野生型30例)ADG患者.术前均行常规扫描以及多壳扩散扫描,经图像预处理和后处理后得到四个定量参数图.使用3D-Slicer软件对肿瘤实性区域、瘤周水肿区域及对侧正常脑白质区进行感兴趣区勾画,比较不同IDH状态的组间差异.使用FeAture Explorer(FAE)软件构建预测模型,在模型的开发过程中考虑了多条流水线组合,包括1种数据降维方法(皮尔森相关系数),4种特征选择方法(多变量方差分析、克鲁斯卡尔-沃利斯检验、递归特征消除和Relief算法)和4种线性分类器(支持向量机、线性判别分析、逻辑回归和引入 LASSO 正则项的逻辑回归),共 16 个流水线.模型之间的比较通过受试者工作特征(receiver operating characteristic,ROC)曲线,综合判别改变指数(integrated discrimination improvement,IDI)、净重分类改善指数(net reclssification improvement,NRI)和留一法交叉验证进行评估.结果 IDH野生型组年龄高于IDH突变型组(P=0.001).只有IDH野生型组的瘤周水肿区方向离散指数(orientation dispersion index,ODI)值低于IDH突变型组(P=0.019),其余参数组间未见差异.对侧白质区和水肿区分别具有最高和最低的ICVF(intra-cellular volume fraction,ICVF)值(P<0.05).通过线性判别分析构建的综合模型诊断效能最高,ROC曲线下面积为0.835(95%CI:0.703~0.941);高于单独使用年龄[0.773(95%CI:0.624~0.894)]和ODI[0.695(95%CI:0.538~0.845)].IDI以及NRI证明了最终模型最高的诊断性能(所有P值均小于0.001).决策曲线分析和校准曲线证明了最终模型的临床净收益和最接近真实数据的分布(Brier分数为0.163).结论 NODDI定量参数可以用来描述ADG的微观环境差异.建立的最终模型可以有效预测成人型弥漫性胶质瘤不同IDH基因状态,且复合模型比单一模型的诊断效能更好.

Objective:To evaluate the differences of tumors with different isocitrate dehydrogenase(IDH)gene states by using the quantitative parameters of neurite orientation dispersion and density imaging(NOODI)and to explore the clinical application value of NOODI in genotyping of adult-type diffuse glioma(ADG).Materials and Methods:Fifty-one patients with adult diffuse glioma(IDH mutant 21 cases,IDH wild type 30 cases)were collected.Routine scanning and multi-shell diffusion scanning were performed before operation,and four quantitative parameter maps were obtained after image preprocessing and post-processing.All patients underwent conventional scan and multi-shell diffusion scan before operation.Four quantitative parametric maps were obtained after image preprocessing and post-processing.The 3D-Slicer was used to delineate the regions of interest in tumor solid areas,peritumoral edema areas,and contralateral normal white matter areas,compared the differences between groups with different IDH status.FeAture Explorer(FAE)software was used to construct the prediction model,and multiple pipeline combinations were considered during the development of the prediction model,including 1 data dimensionality reduction method(Pearson's correlation coefficient),4 feature selection methods(multivariate analysis of variance,Kruskal Wallis test,recursive feature elimination and Relief algorithm)and 4 linear classifiers(support vector machine,linear discriminant analysis,logistic regression and logistic regression with LASSO regularization term),with a total of 16 pipelines.The comparisons between models were using the receiver operating characteristic(ROC)curve.The final model was evaluated by integrated discrimination improvement(IDI),net reclssification improvement(NRI)index and leave-one-out cross-validation.Results:The age of IDH wild-type group was higher than that of IDH mutant group(P=0.001).Only the orientation dispersion index(ODI)of IDH wild-type group was lower than that of IDH mutant group(P=0.019),and there was no difference among other parameter groups.The contralateral white matter area and edema area had the highest and lowest intra-cellular volume fraction(ICVF)values,respectively(P<0.05).The comprehensive model constructed by linear discriminant analysis has the highest diagnostic efficiency,and the area under ROC curve is 0.835(95%CI:0.703-0.941).It is higher than the age of single use[0.773(95%CI:0.624-0.894)]and ODI[0.695(95%CI:0.538-0.845)].The IDI and NRI prove that the final model has the highest diagnostic performance(all P<0.001).Decision curve analysis and calibration curve prove the clinical net benefit of the final model and the distribution closest to the real data(Brier score is 0.163).Conclusions:The quantitative parameters of NODDI can be used to describe the microenvironment differences of ADG.The final model can effectively predict the different IDH gene states of ADG,and the diagnostic efficiency of the composite model is better than that of the single model.

张驰;吴琼;何金龙;谢生辉;王鹏;王少彧;张华鹏;高阳

内蒙古医科大学附属医院影像诊断科,呼和浩特 010059西门子医疗系统有限公司,上海 200126

临床医学

胶质瘤基因分型异柠檬酸脱氢酶神经突方向离散度和密度成像磁共振成像生物标记物

gliomagenotypingisocitrate dehydrogenaseneurite directional dispersion and density imagingmagnetic resonance imagingbiomark

《磁共振成像》 2024 (004)

38-44 / 7

Science and Technology Plan Project of Inner Mongolia Autonomous Region(No.2019GG047). 内蒙古自治区科技计划项目(编号:2019GG047)

10.12015/issn.1674-8034.2024.04.007

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