Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors

• Published in: Journal of magnetic resonance imaging Vol. 60; no. 3; pp. 923 - 938
• Main Authors: Moodi, Farzan, Khodadadi Shoushtari, Fereshteh, Ghadimi, Delaram J., Valizadeh, Gelareh, Khormali, Ehsan, Salari, Hanieh Mobarak, Ohadi, Mohammad Amin Dabbagh, Nilipour, Yalda, Jahanbakhshi, Amin, Rad, Hamidreza Saligheh
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2024; Wiley Subscription Services, Inc
• Subjects: Adult; Aged; Algorithms; artificial intelligence; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumors; Central Nervous System Neoplasms - classification; Central Nervous System Neoplasms - diagnostic imaging; Central Nervous System Neoplasms - pathology; Classification; Comparative studies; Criteria; Data analysis; Data augmentation; Female; Field strength; Glioma; Glioma - classification; Glioma - diagnostic imaging; Glioma - pathology; Humans; Image Processing, Computer-Assisted - methods; Machine Learning; Magnetic Resonance Imaging - methods; Male; Middle Aged; Neoplasm Grading; Performance assessment; Performance measurement; Radiomics; Reproducibility of Results; Retrospective Studies; ROC Curve; Statistical analysis; Statistical tests; Tumors; WHO CNS tumor classification; World Health Organization; neoplasm grading; radiomics; glioma; machine learning; WHO CNS tumor classification; artificial intelligence
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.29146

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