IMAGING SURROGATE SIGNATURE OF THE GLIOMA EPIGENETIC LIQUID BIOPSY (GELB) SCORE FOR NON-INVASIVE DETECTION OF TUMOR PROGRESSION
Recommended Citation
Pitarch-Abaigar C, Hashemi AS, Adap S, Pease M, Gatson N, Parker J, Griffith B, Lee I, Walbert T, Snyder J, Noushmehr H, Bakas S. IMAGING SURROGATE SIGNATURE OF THE GLIOMA EPIGENETIC LIQUID BIOPSY (GELB) SCORE FOR NON-INVASIVE DETECTION OF TUMOR PROGRESSION. Neuro Oncol 2025; 27(Supplement 5):v43.
Document Type
Conference Proceeding
Publication Date
11-11-2025
Publication Title
Neuro Oncol
Keywords
magnetic resonance imaging, edema, heterogeneity, biological markers, disease progression, glioma, necrosis, diagnostic imaging, neoplasms, epigenetics, tumor progression, fluid attenuated inversion recovery, performance measures, multiparametric magnetic resonance imaging, datasets, radiomics, liquid biopsy, proof of concept studies, imaging genomics
Abstract
BACKGROUND: Gliomas are characterized by clinical and molecular heterogeneity and typically harbor a poor prognosis, particularly in high-grade forms. While multi-parametric Magnetic Resonance Imaging (mpMRI) assessment remains the clinical standard for monitoring disease progression, it is subjective and prone to misidentifying treatment-related changes as true progression. The Glioma epigenetic Liquid Biopsy (GeLB) score is a non-invasive blood-based biomarker capable of detecting tumor progression earlier than MRI. Here, we seek an objective radiomics-based surrogate signature of the GeLB score, towards democratizing GeLB’s detecting capability without the need for epigenetic analysis. METHODS: We retrospectively analyzed mpMRI data (T1, T1ce, T2, T2-FLAIR) of 65 longitudinal paired GeLB-mpMRI samples (72.31% glioma vs 27.69% non-glioma), from 18 patients. GeLB over 50% defined glioma, and lower values non-glioma. 521 radiomic descriptors were extracted from i) the whole tumor (WT) defined by the abnormal T2-FLAIR envelope, and ii) the peritumoral edematous/infiltrated subregion (WT excluding enhancement and necrosis). A Support Vector Machine classifier was trained to distinguish GeLB-defined glioma/non-glioma, using sequential feature selection to eliminate redundant descriptors over 5-fold cross-validation. To mitigate overfitting and reduce selection bias associated with the limited sample size, performance metrics were averaged across five independent runs. RESULTS: For edema-based descriptors, our signature yielded mean accuracy=67.69% (±0.102), sensitivity=80% (±0.163), and specificity=40% (±0.255). WT-based descriptors, slightly improved accuracy=73.85% (±0.078), and sensitivity=91.11% (±0.130), with comparable specificity=35% (±0.122). CONCLUSIONS: Our findings support radiomic descriptors derived from clinical routine mpMRI sequences as promising imaging surrogates for the GeLB score, offering a potential non-invasive approach to improved progression detection, while obviating the need for epigenetic analysis. Although the specificity remains modest and the dataset limited in size, this proof-of-concept study highlights the potential of radiogenomics for improved response assessment. Future work will focus on validating these findings in larger, multi-institutional cohorts and leveraging more sophisticated descriptors.
Volume
27
Issue
Supplement 5
First Page
v43
