Medical School of Chinese PLA, Beijing, China.
Department of Radiology, The 1st Medical Centre, Chinese PLA General Hospital, Beijing, China.
J Magn Reson Imaging. 2021 May;53(5):1399-1407. doi: 10.1002/jmri.27434. Epub 2020 Nov 12.
Accurate and noninvasive detection of isocitrate dehydrogenase (IDH, including IDH1 and IDH2) status is clinically meaningful for molecular stratification of glioma, but remains challenging.
To establish a model for classifying IDH status in gliomas based on multiparametric MRI.
Retrospective, radiomics.
In all, 105 consecutive cases of grade II-IV glioma with 50 IDH1 or IDH2 mutant (IDHm) and 55 IDH wildtype (IDHw) were separated into a training cohort (n = 73) and a test cohort (n = 32).
FIELD STRENGTH/SEQUENCE: Contrast-enhanced T -weighted (CE-T W), T -weighted (T W), and arterial spin labeling (ASL) images were acquired at 3.0T.
Two doctors manually labeled the volume of interest (VOI) on CE-T W, then T W and ASL were coregistered to CE-T W. A total of 851 radiomics features were extracted on each VOI of three sequences. From the training cohort, all radiomics features with age and gender were processed by the Mann-Whitney U-test, Pearson test, and least absolute shrinkage and selection operator to obtain optimal feature groups to train support vector machine models. The accuracy and area under curve (AUC) of all models for classifying the IDH status were calculated on the test cohort. Two subtasks were performed to verify the efficiency of texture features and the Pearson test in IDH status classification, respectively.
The permutation test with Bonferroni correction; chi-square test.
The accuracy and AUC of the classifier, which combines the features of all three sequences, achieved 0.823 and 0.770 (P < 0.05), respectively. The best model established by texture features only had an AUC of 0.819 and an accuracy of 0.761. The best model established without the Pearson test got an AUC of 0.747 and an accuracy of 0.719.
IDH genotypes of glioma can be identified by radiomics features from multiparameter MRI. The Pearson test improved the performance of the IDH classification models.
4 TECHNICAL EFFICACY STAGE: 1.
准确且无创的异柠檬酸脱氢酶（IDH，包括 IDH1 和 IDH2）状态检测对于胶质瘤的分子分层具有重要的临床意义，但仍然具有挑战性。
建立基于多参数 MRI 对胶质瘤 IDH 状态进行分类的模型。
回顾性、放射组学。
共纳入 105 例连续的 II-IV 级胶质瘤患者，其中 50 例 IDH1 或 IDH2 突变（IDHm），55 例 IDH 野生型（IDHw），将其分为训练队列（n=73）和测试队列（n=32）。
场强/序列：在 3.0T 上采集增强 T1 加权（CE-T1W）、T1 加权（T1W）和动脉自旋标记（ASL）图像。
两位医生手动在 CE-T1W 上勾画感兴趣区（VOI），然后将 T1W 和 ASL 与 CE-T1W 配准。在三个序列的每个 VOI 上共提取 851 个放射组学特征。从训练队列中，对所有带有年龄和性别特征的放射组学特征进行曼-惠特尼 U 检验、皮尔逊检验和最小绝对值收缩和选择算子处理，以获得最优特征组来训练支持向量机模型。在测试队列上计算所有模型对 IDH 状态分类的准确性和曲线下面积（AUC）。分别进行了两个子任务来验证纹理特征和 Pearson 检验在 IDH 状态分类中的效率。
置换检验（Bonferroni 校正）；卡方检验。
结合所有三个序列特征的分类器的准确性和 AUC 分别为 0.823 和 0.770（P<0.05）。仅基于纹理特征建立的最佳模型的 AUC 为 0.819，准确性为 0.761。不使用 Pearson 检验建立的最佳模型的 AUC 为 0.747，准确性为 0.719。
可以通过多参数 MRI 的放射组学特征来识别胶质瘤的 IDH 基因型。Pearson 检验提高了 IDH 分类模型的性能。
4 级 技术功效：1 级
