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    Fusion Radiomics Features from Conventional MRI Predict MGMT Promoter Methylation Status in Lower Grade Gliomas. Jiang Chendan,Kong Ziren,Liu Sirui,Feng Shi,Zhang Yiwei,Zhu Ruizhe,Chen Wenlin,Wang Yuekun,Lyu Yuelei,You Hui,Zhao Dachun,Wang Renzhi,Wang Yu,Ma Wenbin,Feng Feng European journal of radiology PURPOSE:The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter has been proven to be a prognostic and predictive biomarker for lower grade glioma (LGG). This study aims to build a radiomics model to preoperatively predict the MGMT promoter methylation status in LGG. METHOD:122 pathology-confirmed LGG patients were retrospectively reviewed, with 87 local patients as the training dataset, and 35 from The Cancer Imaging Archive as independent validation. A total of 1702 radiomics features were extracted from three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted and T2-weighted MRI images, including 14 shape, 18 first order, 75 texture, and 744 wavelet features respectively. The radiomics features were selected with the least absolute shrinkage and selection operator algorithm, and prediction models were constructed with multiple classifiers. Models were evaluated using receiver operating characteristic (ROC). RESULTS:Five radiomics prediction models, namely, 3D-CE-T1-weighted single radiomics model, T2-weighted single radiomics model, fusion radiomics model, linear combination radiomics model, and clinical integrated model, were built. The fusion radiomics model, which constructed from the concatenation of both series, displayed the best performance, with an accuracy of 0.849 and an area under the curve (AUC) of 0.970 (0.939-1.000) in the training dataset, and an accuracy of 0.886 and an AUC of 0.898 (0.786-1.000) in the validation dataset. Linear combination of single radiomics models and integration of clinical factors did not improve. CONCLUSIONS:Conventional MRI radiomics models are reliable for predicting the MGMT promoter methylation status in LGG patients. The fusion of radiomics features from different series may increase the prediction performance. 10.1016/j.ejrad.2019.108714
    Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain. Su Xiaorui,Chen Ni,Sun Huaiqiang,Liu Yanhui,Yang Xibiao,Wang Weina,Zhang Simin,Tan Qiaoyue,Su Jingkai,Gong Qiyong,Yue Qiang Neuro-oncology BACKGROUND:Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas. METHODS:This single-institution retrospective study included 100 patients with midline gliomas, including 40 patients with H3 K27M mutations and 60 wild-type patients. Radiomics features were extracted from fluid-attenuated inversion recovery images. Prior to autoML analysis, the dataset was randomly stratified into separate 75% training and 25% testing cohorts. The Tree-based Pipeline Optimization Tool (TPOT) was applied to optimize the machine learning pipeline and select important radiomics features. We compared the performance of 10 independent TPOT-generated models based on training and testing cohorts using the area under the curve (AUC) and average precision to obtain the final model. An independent cohort of 22 patients was used to validate the best model. RESULTS:Ten prediction models were generated by TPOT, and the accuracy obtained with the best pipeline ranged from 0.788 to 0.867 for the training cohort and from 0.60 to 0.84 for the testing cohort. After comparison, the AUC value and average precision of the final model were 0.903 and 0.911 in the testing cohort, respectively. In the validation set, the AUC was 0.85, and the average precision was 0.855 for the best model. CONCLUSIONS:The autoML classifier using radiomics features of conventional MR images provides high discriminatory accuracy in predicting the H3 K27M mutation status of midline glioma. 10.1093/neuonc/noz184
    Radiomics strategy for glioma grading using texture features from multiparametric MRI. Tian Qiang,Yan Lin-Feng,Zhang Xi,Zhang Xin,Hu Yu-Chuan,Han Yu,Liu Zhi-Cheng,Nan Hai-Yan,Sun Qian,Sun Ying-Zhi,Yang Yang,Yu Ying,Zhang Jin,Hu Bo,Xiao Gang,Chen Ping,Tian Shuai,Xu Jie,Wang Wen,Cui Guang-Bin Journal of magnetic resonance imaging : JMRI BACKGROUND:Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays. PURPOSE/HYPOTHESIS:To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps. STUDY TYPE:Retrospective; radiomics. POPULATION:A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively. FIELD STRENGTH/SEQUENCE:3.0T MRI/T -weighted images before and after contrast-enhanced, T -weighted, multi-b-value diffusion-weighted and 3D arterial spin labeling images. ASSESSMENT:After multiparametric MRI preprocessing, high-throughput features were derived from patients' volumes of interests (VOIs). The support vector machine-based recursive feature elimination was adopted to find the optimal features for low-grade glioma (LGG) vs. high-grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency. STATISTICAL TESTS:Student's t-test or a chi-square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist. RESULTS:Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI. DATA CONCLUSION:Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision-making for patients with varied glioma grades. LEVEL OF EVIDENCE:3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518-1528. 10.1002/jmri.26010
    High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management. Li Jing,Liu Siyun,Qin Ying,Zhang Yan,Wang Ning,Liu Huaijun PloS one OBJECTIVE:To investigate the performance of high-order radiomics features and models based on T2-weighted fluid-attenuated inversion recovery (T2 FLAIR) in predicting the immunohistochemical biomarkers of glioma, in order to execute a non-invasive, more precise and personalized glioma disease management. METHODS:51 pathologically confirmed gliomas patients committed in our hospital from March 2015 to June 2018 were retrospective analysis, and Ki-67, vimentin, S-100 and CD34 immunohistochemical data were collected. The volumes of interest (VOIs) were manually sketched and the radiomics features were extracted. Feature reduction was performed by ANOVA+ Mann-Whiney, spearman correlation analysis, least absolute shrinkage and selection operator (LASSO) and Gradient descent algorithm (GBDT). SMOTE technique was used to solve the data bias between two groups. Comprehensive binary logistic regression models were established. Area under the ROC curves (AUC), sensitivity, specificity and accuracy were used to evaluate the predict performance of models. Models reliability were decided according to the standard net benefit of the decision curves. RESULTS:Four clusters of significant features were screened out and four predicting models were constructed. AUC of Ki-67, S-100, vimentin and CD34 models were 0.713, 0.923, 0.854 and 0.745, respectively. The sensitivities were 0.692, 0.893, 0.875 and 0.556, respectively. The specificities were: 0.667, 0.905, 0.722, and 0.875, with accuracy of 0.660, 0.898, 0.738, and 0.667, respectively. According to the decision curves, the Ki-67, S-100 and vimentin models had reference values. CONCLUSION:The radiomics features based on T2 FLAIR can potentially predict the Ki-67, S-100, vimentin and CD34 expression. Radiomics model were expected to be a computer-intelligent, non-invasive, accurate and personalized management method for gliomas. 10.1371/journal.pone.0227703