Yan Ren1, Xi Zhang2, Wenting Rui1, Haopeng Pang1, Tianming Qiu3, Jing Wang1, Qian Xie1, Teng Jin1, Hua Zhang1, Hong Chen4, Yong Zhang5, Hongbing Lu2, Zhenwei Yao1, Junhai Zhang1, Xiaoyuan Feng1. 1. Department of Radiology, Huashan Hospital, Fudan University, Shanghai, P.R. China. 2. Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China. 3. Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, P.R. China. 4. Division of Neuropathology, Department of Pathology, Huashan Hospital, Fudan University, Shanghai, P.R. China. 5. GE Healthcare, MR Research, No. 1 Huatuo Road, Shanghai, P.R. China.
Abstract
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), T2 /fluid-attenuated inversion recovery (T2 FLAIR), and diffusion-weighted imaging (DWI). ASSESSMENT: In all, 265 high-throughput radiomic features were extracted on each tumor volume of interest from T2 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 T2 Flair, ADC, eADC, and CBF and six features from T2 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.
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), T2 /fluid-attenuated inversion recovery (T2 FLAIR), and diffusion-weighted imaging (DWI). ASSESSMENT: In all, 265 high-throughput radiomic features were extracted on each tumor volume of interest from T2 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 T2 Flair, ADC, eADC, and CBF and six features from T2 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.