Glioma Tumor Grading Using Radiomics on Conventional Mri: A Comparative Study of Who 2021 and Who 2016 Classification of Central Nervous Tumors Publisher



Moodi F^{1, 2} ; Khodadadi Shoushtari F¹ ; Ghadimi DJ^{1, 3} ; Valizadeh G¹ ; Khormali E⁴ ; Salari HM¹ ; Ohadi MAD^{5, 6} ; Nilipour Y⁷ ; Jahanbakhshi A⁸ ; Rad HS^{1, 9}
Source: Journal of Magnetic Resonance Imaging Published:2023

Abstract

Background: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored. Purpose: To assess the performance of ML in classifying glioma tumor grades based on various WHO criteria. Study Type: Retrospective. Subjects: A neuropathologist regraded gliomas of 237 patients into WHO 2016 and 2021 from 2007 criteria. Field Strength/Sequence: Multicentric 0.5 to 3 Tesla; pre- and post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery. Assessment: Radiomic features were selected using random forest-recursive feature elimination. The synthetic minority over-sampling technique (SMOTE) was implemented for data augmentation. Stratified 10-fold cross-validation with and without SMOTE was used to evaluate 11 classifiers for 3-grade (2, 3, and 4; WHO 2016 and 2021) and 2-grade (low and high grade; WHO 2007 and 2021) classification. Additionally, we developed the models on data randomly divided into training and test sets (mixed-data analysis), or data divided based on the centers (independent-data analysis). Statistical Tests: We assessed ML classifiers using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Top performances were compared with a t-test and categorical data with the chi-square test using a significance level of P < 0.05. Results: In the mixed-data analysis, Stacking Classifier without SMOTE achieved the highest accuracy (0.86) and AUC (0.92) in 3-grade WHO 2021 grouping. The results of WHO 2021 were significantly better than WHO 2016 (P-value<0.0001). In the 2-grade analysis, ML achieved 1.00 in all metrics. In the independent-data analysis, ML classifiers showed strong discrimination between grade 2 and 4, despite lower performance metrics than the mixed analysis. Data Conclusion: ML algorithms performed better in glioma tumor grading based on WHO 2021 criteria. Nonetheless, the clinical use of ML classifiers needs further investigation. Level of Evidence: 3. Technical Efficacy: Stage 2. © 2023 International Society for Magnetic Resonance in Medicine.