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

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 …

Keywords:

Glioma Tumor, Grading, Radiomics, Conventional MRI, WHO 2021, Central Nervous Tumors, Classification

Authors:

Farzan Moodi, Fereshteh Khodadadi Shoushtari, Delaram J Ghadimi, Gelareh Valizadeh, Ehsan Khormali, Hanieh Mobarak Salari, Mohammad Amin Dabbagh Ohadi, Yalda Nilipour, Amin Jahanbakhshi, Hamidreza Saligheh Rad

Journal:

Journal of Magnetic Resonance Imaging