影像组学联合T_(1)CE对Ⅱ、Ⅲ级胶质瘤IDH-1突变状态的预测价值

Prediction of IDH-1 mutation status in WHO gradeⅡandⅢgliomas by radiomics combined with T_(1)-weighted contrast-enhanced image

目的探讨T_(1)加权对比增强成像(T_(1)-weighted contrast-enhanced image,T_(1)CE)的影像组学特征及临床相关参数预测Ⅱ、Ⅲ级胶质瘤的异柠檬酸脱氢酶-1(isocitrate dehydrogenase-1,IDH-1)基因突变的诊断效能。方法选择空军军医大学第二附属医院2017年1月—2019年7月间经手术病理证实的135例患者Ⅱ、Ⅲ级胶质瘤的MRI资料(IDH-1野生型组51例,IDH-1突变型组84例)。利用ITK-SNAP在T_(1)CE上手动绘制全肿瘤强化部分的感兴趣体积(volume of interest,VOI),使用A.K.软件从VOI中提取1044个影像组学特征。采用随机森林(random forest,RF)算法和5折交叉验证法验证影像组学模型以预测Ⅱ、Ⅲ级胶质瘤IDH-1突变的诊断效能。结果发病部位和结节状/环形强化在IDH-1突变型与IDH-1野生型组间差异有统计学意义(均P<0.05),2组间的其他MRI形态学特征差异无统计学意义。影像组学模型的ROC曲线下面积为0.794,灵敏度为61.4%,特异度为76.7%,准确性为70.9%。在RF分类器特征选择之后,选取前30个最优特征对IDH-1突变进行预测,与采用所有组学特征的效能相当,并且明显降低了冗余信息。结论影像组学联合T1CE能有效预测Ⅱ、Ⅲ级胶质瘤的IDH-1突变状态。RF分类器模型在预测IDH-1突变方面具有潜力,将有可能为胶质瘤患者早期诊断和个体化治疗方案提供影像学依据。

Objective To explore the diagnostic efficiency of T_(1)-weighted contrast-enhanced image(T_(1)CE)radiomic features and clinical-related parameters in predicting isocitrate dehydrogenase-1(IDH-1)gene mutations in WHO gradeⅡandⅢgliomas.Methods MRI data of 135 patients with WHO gradeⅡandⅢgliomas(51 cases in the IDH-1 wild type group and 84 cases in the IDH-1 mutant type group)confirmed by surgery and pathology from the Second Affiliated Hospital of Air Force Military Medical University between January 2017 and July 2019 were selected.The volume of interest(VOI)of the whole tumor-enhanced part was manually drawn on T_(1)CE using ITK-SNAP,and 1044 radiomic features from the VOI were extracted by using the software of A.K.software.Random forest(RF)algorithm and 5-fold cross-validation method were used to verify the radiomic model in predicting the diagnostic efficiency of IDH-1 mutations of WHO gradeⅡandⅢgliomas.Results There were statistical differences in the location of the disease and nodular/ring enhancement between IDH-1 mutant and IDH-1 wild-type groups(all P<0.05),but no significant differences in other MRI morphological characteristics between the two groups.The AUC value of the area under the ROC curve from the iradiomic model was 0.794,the sensitivity was 61.4%,the specificity was 76.7%,and the accuracy was 70.9%.After the feature selection of the RF classifier,the first 30 optimal features were selected to predict IDH-1 mutation,which had the same efficiency as all the radiomic features,and significantly reduced the redundant information.Conclusion Radiomics combined with T_(1)CE can effectively predict the IDH-1 mutation status of WHO gradeⅡandⅢgliomas.RF classifier model has the potential to predict IDH-1 mutations,which may provide an imaging basis for early diagnosis and individualized treatment of glioma patients.