基于MRI增强T1WI影像组学预测模型鉴别高级别胶质瘤IDH 1突变型与野生型的价值

目的：探讨基于术前增强T1WI构建的影像组学模型预测高级别胶质瘤患者异柠檬酸脱氢酶-1（IDH 1）突变型与野生型的价值。方法：回顾性分析2012 年6 月至2020 年12 月在温州医科大学附属第五医院行颅脑增强T1WI图像的高级别胶质瘤患者89 例，IDH 1突变型32 例（WHO III级15 例，WHO IV级17例），IDH 1野生型57例（WHO III级12例，WHO IV级45例），按7:3随机分为训练组和验证组。使用A.K软件对原始增强T1WI图像进行影像特征提取，Kruskal-Wallis非参数检验、Spearman相关性分析、LASSO回归及10 倍交叉验证进行特征降维，筛选出最具特征的参数。采用受试者操作特征（ROC）曲线评价模型对高级别胶质瘤IDH 1突变型和IDH 1野生型识别的预测效能。决策曲线分析（DCA）评价模型的获益情况。结果：每位患者增强T1WI图像共提取396个不同的纹理参数，通过LASSO降维及10倍交叉验证筛选，最终得到5个最具特征性纹理参数，并计算得到相应放射值，构建训练组和验证组的预测模型，训练组模型的ROC曲线下面积为0.902（95%CI：0.826～0.978），灵敏度和特异度分别为84.6%和81.8%，验证组模型的ROC曲线下面积为0.844（95%CI：0.676～1.000），敏感度为77.8%，特异度为80.1%。DCA显示影像组学模型在风险阈值0.1～1.0间较大范围内的净收益优于不作处理模型和全部处理模型。结论：基于MRI增强T1WI构建的影像组学模型可有效识别高级别胶质瘤的IDH 1突变型和野生型。

The value of enhanced T1WI radiomics in predicting the IDH 1 genotype of mutant and wild type in highgrade gliomas

Objective: To explore the value of radiomics predictive model based on preoperative enhanced T1WI images in predicting the mutant and wild type of isocitrate dehydrogenase-1 (IDH 1) genotype in patients with high-grade gliomas. Methods: A retrospective analysis was conducted of 89 high-grade glioma patients withcomplete preoperative craniocerebral enhanced T1WI images in the Fifth Affiliated Hospital of Wenzhou Medical University from June 2018 to December 2020, including 32 cases of IDH 1 mutant type (15 cases of WHO grade III, 17 cases of WHO grade IV) and 57 cases of IDH 1 wild type (12 cases of WHO grade III, 45 cases of WHO grade IV). The two groups of patients were randomly divided into training group and validation group with a ratio of 7:3. A.K software was used to extract the texture features from the original enhanced T1WI images,and Kruskal-Wallis test, Spearman correlation analysis, LASSO-regression and ten-fold cross validation wereperformed for feature dimension reduction and identifying the most characteristic parameters to build the predictive model. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model in identifying IDH 1 mutant and IDH 1 wild-type high-grade gliomas. Decision curve analysis (DCA) was used to evaluate the clinical benefit of the model. Results: A total of 396 texture parameters were extracted from theenhanced T1WI images of each patient. Five texture parameters were selected ultimately by LASSO-regression and ten-fold cross validation, and the corresponding radiomics scores (Rad-scroes) were calculated to construct the prediction models of the training group and the validation group. The area under curve (AUC) of ROC of the training group was 0.902 (95%CI: 0.826-0.978), with the sensitivity and specificity being 84.6% and 81.8%, respectively. The AUC of the validation group was 0.844 (95%CI: 0.676-1.000), with the sensitivity and specificity being 77.8% and 80.1%, respectively. DCA showed that the net benefit of the radiomics model was better than that of the untreated model and the all-treated model with a risk threshold ranges from 0.1 to 1.0. Conclusion: The radiomics model based on enhanced T1WI images can effectively identify the IDH 1 mutant and wild type of high-grade glioma.