Radiomics Strategy for Molecular Subtype Stratification of Lower‐Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI

• Published in: Journal of magnetic resonance imaging Vol. 48; no. 4; pp. 916 - 926
• Main Authors: Zhang, Xi, Tian, Qiang, Wang, Liang, Liu, Yang, Li, Baojuan, Liang, Zhengrong, Gao, Peng, Zheng, Kaizhong, Zhao, Bofeng, Lu, Hongbing
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.10.2018
• Subjects: Attenuation; Correlation coefficients; Data processing; Field strength; Glioma; IDH; Image contrast; Image enhancement; Isocitrate dehydrogenase; lower‐grade glioma; Magnetic resonance imaging; Medical imaging; multimodal MRI; Mutation; p53 Protein; Patients; Population (statistical); Population studies; Radiomics; Scanners; Statistical analysis; Statistical tests; Support vector machines; TP53; Variance analysis; radiomics; IDH; TP53; lower-grade glioma; multimodal MRI
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.25960

Background Noninvasive detection of isocitrate dehydrogenase (IDH) and TP53 mutations are meaningful for molecular stratification of lower‐grade gliomas (LrGG). Purpose To explore potential MRI features reflecting IDH and TP53 mutations of LrGG, and propose a radiomics strategy for detecting them. Study Type Retrospective, radiomics. Population/Subjects A total of 103 LrGG patients were separated into development (n = 73) and validation (n = 30) cohorts. Field Strength/Sequence T1‐weighted (before and after contrast‐enhanced), T2‐weighted, and fluid‐attenuation inversion recovery images from 1.5T (n = 37) or 3T (n = 66) scanners. Assessment After data preprocessing, high‐throughput features were derived from patients' volumes of interests of different sequences. The support vector machine‐based recursive feature elimination (SVM‐RFE) was adopted to find the optimal features for IDH and TP53 mutation detection. SVM models were trained and tested on development and validation cohort. The commonly used metric was used for assessing the efficiency. Statistical Tests One‐way analysis of variance (ANOVA), chi‐square, or Fisher's exact test were applied on clinical characteristics to confirm whether significant differences exist between three molecular subtypes decided by IDH and TP53 status. Intraclass correlation coefficients were calculated to assess the robustness of the radiomics features. Results The constituent ratio of histopathologic subtypes was significantly different among three molecular subtypes (P = 0.017). SVM models for detecting IDH and TP53 mutation were established using 12 and 22 optimal features selected by SVM‐RFE. The accuracies and area under the curves for IDH and TP53 mutations on the development cohort were 84.9%, 0.830, and 92.0%, 0.949, while on the validation cohort were 80.0%, 0.792, and 85.0%, 0.869, respectively. Furthermore, the stratified accuracies of three subtypes were 72.8%, 71.9%, and 70%, respectively. Data Conclusion Using a radiomics approach integrating SVM model and multimodal MRI features, molecular subtype stratification of LGG patients was implemented through detecting IDH and TP53 mutations. The results suggested that the proposed approach has promising detecting efficiency and T2‐weighted image features are more important than features from other images. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:916–926.