Nimg-28. Increased Mutation Burden (hypermutation) in Gliomas Is Associated With a Unique Radiomic Texture Signature in Magnetic Resonance Imaging
Year: 2017
Type: article
Study design: Observational Study
Abstract: Increased mutation burden (hypermutation) has been reported in low and high-grade gliomas. Hypermutation is typically due to failure of DNA repair machinery such as mismatch repair (MMR) or disruption of DNA fidelity due to mutations in DNA polymerase genes (POLE and POLD). The incidence of hypermutation in high grade gliomas (mainly glioblastoma (GB)) occurs largely at recurrence and induces resistance to temozolomide therapy. Furthermore, defects in MMR genes in hypermutated GB can abolish and reverse the therapeutic effect of TMZ causing disease progression. Herein, we sought to identify an imaging-based signature for hypermutated gliomas using a radiomics-based approach. We retrospectively analyzed a total of 97 patients with primary gliomas from the University of Texas MD Anderson Cancer Center. Mutation status was determined post-biopsy (stereotactic/excisional) using Next Generation Sequencing (T200 and Foundation 1), and patients were grouped based on their mutation burden; 77 hypomutated (<30 mutations) and 20 hypermutated (>=30 mutations or <30 with MMR gene mutations). Radiomic analysis was performed on the conventional MR images (FLAIR and T1 post-contrast) obtained prior to tumor tissue surgical sampling; and a total of 2480 rotation-invariant radiomic features were extracted using: (i) the first-order histogram and (ii) grey level co-occurrence matrix. The Maximum Relevance Minimum Redundancy technique was used to select the most relevant radiomic features. ROC analysis and leave-one-out cross-validation (LOOCV) were used to assess the performance of the Support Vector Machine (SVM) classifier and AUC, Sensitivity, Specificity, and p-value were obtained. We found 100 radiomic features that can discriminate between hypermutated versus hypomutated gliomas, AUC 100% (CI: 96%-100%), Sensitivity 100%, Specificity 100%, p-value=1.8e-10. Increase in mutation burden seen in post-treatment glioma samples is associated with a unique radiomic texture signature on pre-sampling MRI and can be used to predict mutation burden status non-invasively.
Source: Neuro-Oncology
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2767487517