Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging

Abstract

The IDH somatic mutation status is an important basis for the diagnosis and classification of gliomas. We proposed a “6-Step” general radiomics model to noninvasively predict the IDH mutation status by simultaneously tuning combined multi-sequence MRI and optimizing the full radiomics processing pipeline. Radiomic features (n = 3776) were extracted from multi-sequence MRI (T1, T2, FLAIR, and T1Gd) in low-grade gliomas (LGGs), and a total of 45,360 radiomics pipeline were investigated according to different settings. The predictive ability of the general radiomics model was evaluated with regards to accuracy, stability, and efficiency. Based on numerous experiments, we finally reached an optimal pipeline for classifying IDH mutation status, namely the T2+FLAIR combined multi-sequence with the wavelet image filter, mean data normalization, PCC dimension reduction, RFE feature selection, and SVM classifier. The mean and standard deviation of AUC, accuracy, sensitivity, and specificity were 0.873 ± 0.05, 0.876 ± 0.09, 0.875 ± 0.11, and 0.877 ± 0.15, respectively. Furthermore, 14 radiomic features that best distinguished the IDH mutation status of the T2+FLAIR multi-sequence were analyzed, and the gray level co-occurrence matrix (GLCM) features were shown to be of high importance. Apart from the promising prediction of the molecular subtypes, this study also provided a general tool for radiomics investigation.

Machine learning models based on multi-parameter MRI radiomics for prediction of molecular glioblastoma: a new study based on the 2021 World Health Organization classification

“…As the most popular texture features for measuring image heterogeneity, GLCM not only reflects the distribution of gray levels, but also reveals the second-order information of the adjacency relationship between voxels and surrounding voxels (32,33). He et al (34) developed machine learning models based on multisequence MRI radiomics features and optimized the full radiomics processing pipeline to non-invasively predict the IDH mutation status of gliomas. The optimal model in their study achieved a superior performance with an AUC of 0.873 in the test group, and GLCM features were also confirmed to be of high importance (34).…”

“…He et al (34) developed machine learning models based on multisequence MRI radiomics features and optimized the full radiomics processing pipeline to non-invasively predict the IDH mutation status of gliomas. The optimal model in their study achieved a superior performance with an AUC of 0.873 in the test group, and GLCM features were also confirmed to be of high importance (34). Although the specific biological significance of these high-dimensional data remains to be further explored, this application of computed abstract features to objectively reveal tumor heterogeneity will be a trend in the future of imaging.…”

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning

“…For example, Alves et al investigated the differentiability of inflammatory lesions and brain tumors using such methods [25]. Radiomics, when coupled with machine learning techniques, has also demonstrated its potential in predicting various molecular markers and clinical outcomes in diverse malignancies [26][27][28]. In this context, machine learning refers to the use or development of algorithms that are able to independently recognize patterns in data and subsequently draw their own conclusions based on the previously learned relationships.…”

The value of multiparametric MRI radiomics in predicting IDH genotype in glioma before surgery

“…Zhang et al. ( 18 ) predicted IDH-M in LGGs preoperatively by multiparametric MRI radiomics model and obtained AUC value of 0.83, with T2-weighted imaging(T2WI) images being the most important.Another study titled “Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging, 2022 ( 24 )” concluded that a multiparametric radiomics model of T2-weighted-fluid-attenuated inversion recovery (T2-FLAIR) is most effective in distinguishing IDH mutation status in low-grade gliomas. However, Niu et al.…”

“…Sun et al ( 25 ) concluded that a combined machine learning algorithm exhibits excellent predictive performance in non-invasively predicting the molecular subtypes of lower-grade glioma (LGG) preoperatively. Several studies have incorporated clinical data into radiomics to build a combined model and found superior results ( 24 – 27 ). Zhou et al ( 28 ) and Tan et al ( 27 ).…”