Abstract
Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include nested leave-one-out cross-validation to select features, train the model, and estimate model performance. The prediction models of MGMT methylation, IDH mutations, 1p/19q co-deletion, ATRX mutation, and TERT mutations achieve a test performance AUC of 0.83 ± 0.04, 0.84 ± 0.03, 0.80 ± 0.04, 0.70 ± 0.09, and 0.82 ± 0.04, respectively. Furthermore, our analysis shows that the fractal features have a significant effect on the predictive performance of MGMT methylation IDH mutations, 1p/19q co-deletion, and ATRX mutations. The performance of our prediction methods indicates the potential of correlating computed imaging features with LGG molecular mutations types and identifies candidates that may be considered potential predictive biomarkers of LGG molecular classification.
Highlights
Diffuse low-grade gliomas (LGG) are World Health Organization (WHO) Grade II and III gliomas
Our study addresses diffuse LGG grading and classification prediction based on molecular mutations using imaging features that are extracted from multimodality raw magnetic resonance imaging (MRI) sequences (T1, contrast-enhanced T1(T1 Gd), T2 Fluid Attenuated Inversion Recovery (FLAIR), and T2) of the anatomically depicted tumor volume, and texture representations of the tumor MRI sequences
The 2016 WHO classification of diffuse LGGs heavily weighs molecular mutations classifying primary brain tumors with particular importance assigned to IDH mutation, 1p/19q co-deletion, ATRX mutation, telomerase reverse transcriptase (TERT) mutations, and methylguanine-DNA methyltransferase (MGMT) methylation
Summary
Diffuse low-grade gliomas (LGG) are World Health Organization (WHO) Grade II and III gliomas. A 1p/19q co-deletion is considered as a molecular marker of oligodendroglioma and is associated with IDH mutation[11] and improved survival[8] This genetic alteration happens when the short arm of chromosome 1 (1p), and the long arm of chromosome 19 (19q) are deleted. ATRX mutation often occurs with IDH mutations and is almost mutually exclusive with 1p/19q co-deletion Another molecular alteration that has a high prevalence of LGG is O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation[17]. Diffuse LGG is known for its heterogeneous characteristic that reveals variances in tumor biology This heterogeneity can be seen through the histological types: astrocytoma, oligoastrocytoma, and oligodendroglioma[4,20], oligoastrocytoma is no longer used when molecular markers are available. The heterogeneity can be characterized by magnetic resonance imaging (MRI) features[21,22,23], which suggests using MRI features as a non-invasive marker in tumor grading and classification[24,25,26,27,28]
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