Document Type
Article
Publication Date
2020
DOI
10.1038/s41598-020-60550-0
Publication Title
Scientific Reports
Volume
10
Issue
1
Pages
1-13
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.
Original Publication Citation
Shboul, Z. A., Chen, J., & Iftekharrudin, K.M. (2020). Prediction of molecular mutations in diffuse low-grade gliomas using MR imaging features. Scientific Reports, 10(1), 1-13. doi:10.1038/s41598-020-60550-0
Repository Citation
Shboul, Zeina A.; Chen, James; and Iftekharrudin, Khan M., "Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas Using MR Imaging Features" (2020). Electrical & Computer Engineering Faculty Publications. 252.
https://digitalcommons.odu.edu/ece_fac_pubs/252
ORCID
0000-0002-1277-4041 (Shboul), 0000-0001-8316-4163 (Iftekharuddin)
Comments
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.