使用多参数磁共振放射组学特征无创预测低级别胶质瘤中的 IDH1 突变和 ATRX 表达缺失。
Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low-Grade Gliomas Using Multiparametric MR Radiomic Features.
机构信息
Department of Radiology, Huashan Hospital, Fudan University, Shanghai, P.R. China.
Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
出版信息
J Magn Reson Imaging. 2019 Mar;49(3):808-817. doi: 10.1002/jmri.26240. Epub 2018 Sep 8.
BACKGROUND
Noninvasive detection of isocitrate dehydrogenase 1 mutation (IDH1(+)) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression ((ATRX(-)) are clinically meaningful for molecular stratification of low-grade gliomas (LGGs).
PURPOSE
To study a radiomic approach based on multiparametric MR for noninvasively determining molecular status of IDH1(+) and ATRX(-) in patients with LGG.
STUDY TYPE
Retrospective, radiomics.
POPULATION
Fifty-seven LGG patients with IDH1(+) (n = 36 with 19 ATRX(-) and 17 ATRX(+) patients) and IDH1(-) (n = 21).
FIELD STRENGTH/SEQUENCE: 3.0T MRI / 3D arterial spin labeling (3D-ASL), T /fluid-attenuated inversion recovery (T FLAIR), and diffusion-weighted imaging (DWI).
ASSESSMENT
In all, 265 high-throughput radiomic features were extracted on each tumor volume of interest from T FLAIR and the other three parametric maps of ASL-derived cerebral blood flow (CBF), DWI-derived apparent diffusion coefficient (ADC), and exponential ADC (eADC). Optimal feature subsets were selected as using the support vector machine with a recursive feature elimination algorithm (SVM-RFE). Receiver operating characteristic curve (ROC) analysis was employed to assess the efficiency for identifying the IDH1(+) and ATRX(-) status.
STATISTICAL TESTS
Student's t-test, chi-square test, and Fisher's exact test were applied to confirm whether intergroup significant differences exist between molecular subtypes decided by IDH1 and ATRX.
RESULTS
Optimal SVM predictive models of IDH1(+) and ATRX(-) were established using 28 features from T Flair, ADC, eADC, and CBF and six features from T Flair, ADC, and CBF. The accuracies/AUCs/sensitivity/specifity/PPV/NPV of predicting IDH1(+) in LGG were 94.74%/0.931/100%/85.71%/92.31%/100%, and those of predicting ATRX(-) in LGG with IDH1(+) were 91.67%/0.926/94.74%/88.24%/90.00%/93.75%, respectively.
DATA CONCLUSION
Using the optimal texture features extracted from multiple MR sequences or parametric maps, a promising stratifying strategy was acquired for predicting molecular subtypes of IDH1 and ATRX in LGGs.
LEVEL OF EVIDENCE
3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;49:808-817.
背景
对异柠檬酸脱氢酶 1 突变（IDH1(+)）和 X 连锁α-地中海贫血/智力低下综合征核表达缺失（ATRX(-)）的非侵入性检测对低级别胶质瘤（LGG）的分子分层具有重要的临床意义。
目的
研究一种基于多参数磁共振的放射组学方法，无创性确定 LGG 患者 IDH1(+)和 ATRX(-)的分子状态。
研究类型
回顾性、放射组学研究。
人群
57 例 IDH1(+)（n=36，其中 19 例 ATRX(-)和 17 例 ATRX(+)）和 IDH1(-)（n=21）LGG 患者。
场强/序列： 3.0T MRI/三维动脉自旋标记（3D-ASL）、T/液体衰减反转恢复（T FLAIR）和弥散加权成像（DWI）。
评估
在每个感兴趣的肿瘤容积上，总共从 T FLAIR 和其他三个参数图（ASL 衍生的脑血流（CBF）、DWI 衍生的表观弥散系数（ADC）和指数 ADC（eADC））中提取了 265 个高通量放射组学特征。使用支持向量机与递归特征消除算法（SVM-RFE）选择最佳特征子集。采用受试者工作特征曲线（ROC）分析评估识别 IDH1(+)和 ATRX(-)状态的效率。
统计检验
学生 t 检验、卡方检验和 Fisher 精确检验用于确认分子亚型由 IDH1 和 ATRX 决定的组间是否存在显著差异。
结果
使用来自 T Flair、ADC、eADC 和 CBF 的 28 个特征以及来自 T Flair、ADC 和 CBF 的 6 个特征，建立了用于预测 IDH1(+)和 ATRX(-)的最佳 SVM 预测模型。LGG 中预测 IDH1(+)的准确性/AUC/敏感性/特异性/PPV/NPV 分别为 94.74%/0.931/100%/85.71%/92.31%/100%，LGG 中 IDH1(+)伴 ATRX(-)的预测准确性/AUC/敏感性/特异性/PPV/NPV 分别为 91.67%/0.926/94.74%/88.24%/90.00%/93.75%。
数据结论
使用来自多个 MR 序列或参数图的最佳纹理特征，可以获得一种有前途的分层策略，用于预测 LGG 中 IDH1 和 ATRX 的分子亚型。
证据水平
3 技术功效阶段： 2 J. Magn. Reson. Imaging 2019;49:808-817.
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