3628
Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status1Tianjin Medical University Graduate School, Tianjin, China, 2Department Of Imaging Diagnosis, Affiliated Hospital Of Inner Monglia Medical University, Hohhot, China, 3MR Scientific Marketing, Siemens Healthineers, Shanghai, China, 4GE Healthcare, Shanghai, China, 5Inner Monglia Medical University, Hohhot, China
Synopsis
Objective:To compare the performance of clinical model, radiomics model, and combined model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q) of glioma.Methods: 81 glioma patients confirmed by histology were enrolled in this study. The predictive performance of each model was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA).Results: The mixed model showed the highest performance in each genic phenotype (IDH AUC = 0.93, MGMT AUC=0.88, TERT AUC=0.76, 1p/19q AUC=0.71).Conclusion: The mixed model is an effective tool to distinguish genic phenotype of brain glioma which have highest diagnostic efficiency than other models.
Abstract
Objective To compare the performance of clinical features(age, sex, WHO grade etc.), radiomics features about conventional MR image(T2WI, T1WI, DWI, ADC, CE-MRI (contrast enhancement)), and a combined multiple features model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q) of glioma.Methods In this retrospective analysis, 81 glioma patients confirmed by histology were enrolled in this study. Differences in the clinical and imaging characteristics between groups of each genic phenotype were evaluated using the independent samples t test, the Mann-Whitney U test, and the chi-squared or Fisher’s exact tests, as appropriate. Automatic preprocessing was standardized for each case involving intensity normalization, resampling, and discretization. Tumor segmentation was performed manually on every slice that the tumor was visualized using T2WI images by ITK-snap software. A VOI was generated encompassing the entire region of T2WI hyperintensity and overlaid onto coregistered T1W, T1W+c, DWI and ADC datasets for radiomics texture analysis. A total of 107 radiomics features were extracted from each sequence on Pyradiomics software. Then, univariate analysis and LASSO regression model were used to data dimension reduction, feature selection, and radiomics signature building. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, combined radiomics model. The combined radiomics model included T2WI, T1WI, DWI, ADC and CE-MRI radiomics features. Clinical features were combined with a radiomics features to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA).
Results The demographic data including age, sex, tumor grade, and enhancement status are grouped based on biomarker mutation status and summarized in Table 1.Clinical and morphological characteristics have statistical significance of each biomarker are summarized in Table 2(Using univariate analysis, the characteristics of P <0.05 were retained). The mixed model showed the highest performance in each genic phenotype (IDH AUC = 0.93, MGMT AUC=0.88, TERT AUC=0.76, 1p/19q AUC=0.71) (Fig.1,2). The performance of the combined radiomics model was higher than the clinical model in the most of genic phenotypes, including MGMT, TERT, 1p/19q (Fig.1,2). Decision curve analysis showed the mixed model can better identify each genic phenotype of glioma (Fig.3).
Conclusion The mixed model is an effective tool to distinguish genic phenotype of brain glioma which have highest diagnostic efficiency than other models.
Acknowledgements
I am grateful to my wife and colleagues for their great support to my research work.
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Figures
Table 1 Demographic Data for Each Glioma Biomarker
Table 2 Multivariate logistic regression analysis for each Glioma Biomarker
Figure 1. Receiver-operating characteristic (ROC) curves for prediction of each biomarker status. Combined model (COMB) which integrate clinical (clinical model, which combined clinical and imaging morphological characteristics) and radiomics features (all, combined radiomics model) shows significant improvement in predicting each biomarker status, especially in the group of IDH (0.928) and MGMT (0.878).
Figure 2. The bar (a)and box(b) chart of Radscore. The two charts showed a better performance in predicting each biomarker status, especially in group of IDH and MGMT.
Figure 3. The decision curve analysis for combined radiomics model (all, combined all radiomic features from each sequence) and combined model (COMB, combined clinical and radimics features). The Y-axis represents the net benefit.