Prediction of 1p/19q codeletion status in diffuse lower-grade glioma using multimodal MRI radiomics / 南方医科大学学报

OBJECTIVE@#To develop a noninvasive method for prediction of 1p/19q codeletion in diffuse lower-grade glioma (DLGG) based on multimodal magnetic resonance imaging (MRI) radiomics.@*METHODS@#We collected MRI data from 104 patients with pathologically confirmed DLGG between October, 2015 and September, 2022. A total of 535 radiomics features were extracted from T2WI, T1WI, FLAIR, CE-T1WI and DWI, including 70 morphological features, 90 first order features, and 375 texture features. We constructed logistic regression (LR), logistic regression least absolute shrinkage and selection operator (LRlasso), support vector machine (SVM) and Linear Discriminant Analysis (LDA) radiomics models and compared their predictive performance after 10-fold cross validation. The MRI images were reviewed by two radiologists independently for predicting the 1p/19q status. Receiver operating characteristic curves were used to evaluate classification performance of the radiomics models and the radiologists.@*RESULTS@#The 4 radiomics models (LR, LRlasso, SVM and LDA) achieved similar area under the curve (AUC) in the validation dataset (0.833, 0.819, 0.824 and 0.819, respectively; P>0.1), and their predictive performance was all superior to that of resident physicians of radiology (AUC=0.645, P=0.011, 0.022, 0.016, 0.030, respectively) and similar to that of attending physicians of radiology (AUC=0.838, P>0.05).@*CONCLUSION@#Multiparametric MRI radiomics models show good performance for noninvasive prediction of 1p/19q codeletion status in patients with in diffuse lower-grade glioma.

Expression of AEBP1 and its prognostic role in glioma based on the data-mining of TCGA and GTEx databases / 西安交通大学学报(医学版)

【Objective】 To explore the expressions of adipocyte enhancer binding protein 1 （AEBP1） gene and its isoforms in different types of gliomas， and the influence of AEBP1 gene on the prognosis of patients with different types of gliomas. 【Methods】 We used the GEPIA2 visual network analysis tool to analyze AEBP1 gene expression levels in the tumor tissues of glioblastomas （GBM， including classical， mesenchymal， neural， and proneural ones） and low-grade gliomas （LGG， including astrocytoma， oligoastrocytoma， oligodendroglioma） in the TCGA database and normal human tissue samples in the TCGA and GTEx databases by one-way ANOVA. The distribution trend of isoforms of AEBP1 gene in gliomas was analyzed using the violin plot. The Kaplan-Meier survival curve was drawn and the Logrank test was used to analyze the influence of AEBP1 gene expression in GBM and LGG tumor tissues on the prognosis of glioma patients. 【Results】 The expression of AEBP1 in the tumor tissues of overall GBM and the four types of GBM was higher than that in the normal control tissues （P<0.05）. The expression of AEBP1 in astrocytoma and oligodendrocyte astrocytoma tumor tissues was higher than that in normal control tissues （P<0.05）. There were nine isoforms of AEBP1 gene in GBM and LGG， and the expression level in GBM was higher. The overall survival （OS） of the AEBP1 low expression group of GBM patients and the proneuronal GBM patients was better than that of the high expression group （P<0.05）. The OS and progression-free survival of LGG patients and the AEBP1 low-expression group of astroglioma were better than those of the high-expression group （P<0.05）. 【Conclusion】 AEBP1 has an important clinical value in the pathogenesis and development of GBM and LGG， and thus can be used as a diagnostic marker and a candidate gene for targeted therapy.

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. MATERIALS AND METHODS: Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade. RESULTS: The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68). CONCLUSION: Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.