多模态MRI影像组学随机森林模型预测术前大脑胶质瘤IDH1基因表达类型效能的初步探讨

A preliminary study on prediction efficacy of multimodal MRI-based radiomics in combination with random forest model for preoperative glioma IDH1 gene type expression

目的初步探讨多模态MRI影像组学随机森林模型对术前大脑胶质瘤IDH1基因表达类型的预测效能。方法回顾性分析2015年5月至2019年1月经宁波市第一医院手术病理证实的108例大脑胶质瘤患者的MRI影像资料,包括T1WI、T2WI、液体衰减反转恢复(FLAIR)序列、DWI序列及T1WI增强序列。其中IDH1突变型47例,IDH1野生型61例,按随机森林模型7∶3比例分割要求,分为训练组(73例)与验证组(35例),应用R语言统计软件分别对IDH1突变型和IDH1野生型两组患者一般临床资料和常规MRI形态特征参数进行统计分析,采用单因素分析结合多因素逻辑回归分析法筛选(P<0.05)IDH1突变型独立预测因子,用于构建常规MRI形态特征随机森林诊断模型。应用MaZda影像组学软件于5个序列肿瘤瘤体最大层面上手动勾画ROI,提取包括内有关灰度共生矩阵(GLCM)、游程检验(RUN)、绝对梯度(GRA)、自回归模型(ARM)及小波变换(WAV)影像组学特征,使用最小绝对收缩和选择算法(LASSO)与10折交叉验证进行影像组学特征降维,最后将筛选后的影像组学标签结合常规形态特征独立预测因子共同构建多模态MRI影像组学随机森林诊断模型,并使用验证数据集分别评估两个模型预测的准确率和诊断效能,绘制ROC曲线动态评估两个模型预测的敏感和特异度,并使用ROC曲线下面积(AUC)统计指标量化两模型的预测效能,利用不同结局下模型分类错误率以及随机森林袋外数据(OOB)分类错误率评估随机森林模型的稳定性。结果常规MRI形态学特征单因素分析及多因素回归分析法结果显示瘤周水肿、囊变、增强3个变量是鉴别IDH1表达类型的独立预测因子(P<0.05);LASSO算法与10折交叉验证进行影像组学特征降维筛选后剩余特征纹理参数6个:T2WI序列的小波变换高频系数(WavEnHH_s-4),T1增强序列的WavEnHH_s-4、熵值[S(5,0)Entropy]、灰度均匀性度量逆差距[S(4,4)InvDfMom],FLAIR序列的熵差[S(1,-1)DifEntrp]、灰度均匀性度量逆差距[S(1,1)InvDfMom]。多模态MRI影像组学诊断模型不同结局分类的错误率及随机森林袋外数据分类错误率最终稳定在10%。特征性变量重要性评价图准确率降低指数和基尼指数结果一致,显示除水肿、增强、囊变3大常规MRI形态特征对模型有重要作用外,影像组学标签变量也起到了关键性作用。验证数据集对诊断模型分别进行效能评估,ROC曲线结果显示常规MRI形态特征诊断模型准确率为82.7%、特异度为68.4%、敏感度为90.9%、AUC为0.835,多模态影像组学模型诊断准确率为88.5%,特异度为89.5%,敏感度为87.8%,AUC提高至0.956。结论结合多模态MRI影像组学标签的诊断模型更能从定量角度提高术前大脑胶质瘤IDH1基因表达类型的预测效能。

Objective To preliminarily analyze the prediction efficiency of multimodal MRI-based radiomics model for preoperative glioma IDH1 gene expression type. Methods The MRI data of 108 surgery-proven glioma patients from May 2015 to January 2019 were retrospectively analyzed, and the MRI data included axial T1WI,T2WI,fluid attenuated inversion recovery (FLAIR),DWI imaging and enhanced T1WI sequence.Forty-seven cases were IDH1 mutant type, and 61 cases were IDH1 wild type. All patients were divided into training and validation groups according to the 7∶3 ratio of the random forest model. Seventy-three cases were in training group, and 35 cases were in validation group. Independent predictors of IDH1 mutation were screened by univariate analysis combined with multivariate logistic regression analysis (P<0.05) in order to construct a random forest diagnosis model of general clinical information and conventional MRI morphological features.General clinical information and conventional MRI morphological features included gender, age, umbers of cases of left and right hemispheres, location of tumors, maximum diameter of tumors, peritumoral edema, intratumoral cystic degeneration, enhancement and ADC value of tumors. The ROI was manually outlined by MaZda software in the most obvious level of 5 sequences of tumor mass and the radiomics features were extracted, including the gray-level co-occurrence matrix(GLCM), the run-length matrix(RUN), the absolute gradient(GRA),the auto-regressive model(ARM) and wavelets transform(WAV). The least absolute shrinkage and selection operator (LASSO)regression were used to select image radiomics features with a method of 10 fold cross -validation and to reduce the dimensions. The screened image radiomics labels were combined with the conventional morphological feature independent predictors to construct a multimodal MRI-based random forest model, and the validation data set was used to evaluate the accuracy and diagnostic efficiency of each model. The sensitivity and specificity of conventional MRI morphological feature model and multimodal MRI-based radiomics prediction model were evaluated dynamically by drawing ROC curves, and the prediction efficiency of the two models was quantified by using AUC statistical indicators. The model classification error rate under different outcomes and the classification error rate of out of bag(OOB)were used to evaluate the stability of the multimodal MRI-based random forest model. The contribution rate of each variable to the model was reflected by the characteristic variables importance assessment map. Results Univariate regression analysis of the conventional MRI morphological characteristics showed that peritumoral edema, cystic degeneration and enhancement were the three independent predictors of IDH1 gene expression (P<0.01). LASSO algorithm and 10-fold cross-validation identified six robust radiomic features including high frequency coefficients of wavelet transform (WavEnHH_s-4) of T2WI, S(4,4) inverse difference of gray uniformity measurement (InvDfMom),S(5,0) Entropy (entropy),WavEnHH_s-4 of T1WI enhancement, S(1,1) InvDfMom,S(1,-1) Entropy Difference (DifEntrp)of Flair.The error rate of classification for different outcomes and classification error rate of random forest OOB data of multimodal MRI radiomics diagnosis model finally stabilized at 10%. The results of Characteristic Variable Importance Assessment Map: Mean Decrease Accuracy and Mean Decrease Gini index were consistent, which showed that besides three conventional MRI morphological predictors peritumoral edema, enhancement and cystic degeneration, the radiomics labels also played a key role in the model. The results of ROC curve showed that the accuracy, specificity, sensitivity and AUC of conventional MRI morphological feature model were 82.7%, 68.4%, 90.9% and 0.835, respectively, and those of multimodal MRI-based radiomics model were 88.5%, 89.5%, 87.8% and 0.956 respectively. Conclusion Multimodal MRI-based radiomics random forest model can improve the predictive efficiency of preoperative glioma IDH1 gene expression type more quantitatively.