# Using Delta MRI-Based Radiomics for Monitoring Early Peri-Tumoral Changes in a Mouse Model of Glioblastoma: Primary Study

## TL;DR

This study uses advanced MRI techniques to detect early tumor spread in a mouse model of glioblastoma, revealing changes not visible on standard scans.

## Contribution

The study introduces delta radiomics as a novel method for longitudinal monitoring of peritumoral changes in glioblastoma.

## Key findings

- T2W texture and T2 map first-order features showed high sensitivity in detecting peritumoral changes over time.
- Delta radiomics outperformed static imaging in capturing early signs of tumor invasion.
- The method could serve as a robust biomarker for monitoring glioblastoma progression.

## Abstract

Glioblastoma is an aggressive brain tumor that spreads into healthy brain tissue, making it difficult to detect and treat. In this study, researchers used advanced MRI techniques to track subtle changes in the brain surrounding the tumor over time in a mouse model of glioblastoma. The goal was to identify early signs of tumor invasion that might not be visible on traditional imaging. The study focused on changes in specific MRI features and compared them over different time points to better understand how the tumor spreads. They found that certain MRI features, particularly those related to texture changes, could detect microscopic tumor invasion in areas that looked normal on standard scans. This approach, known as delta radiomics, was more effective than static imaging techniques previously used. These findings suggest that tracking changes in brain tissue over time with specialized MRI scans could help detect glioblastoma at earlier stages, offering potential for better diagnosis and treatment in the future.

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor marked by diffuse infiltration into surrounding brain tissue. The peritumoral zone often appears normal on imaging yet harbors microscopic invasion. While perfusion-based studies, such as arterial spin labeling (ASL), have profiled this region, longitudinal radiomic monitoring remains limited. This study investigates delta radiomics using multiparametric MRI (mpMRI) in a GBM mouse model to track subtle peritumoral changes over time. Methods: A G7 GBM xenograft model was established in nine nude mice, imaged at 9- and 12 weeks post-implantation using MRI (T1W, T2W, T2 mapping, DWI-ADC, FA, and ASL) and co-registered histopathology (H&E, HLA staining). Tumor and peritumoral regions were manually segmented, and 107 radiomic features (shape, first-order, texture) were extracted per sequence and histology. The delta features were calculated and compared between timepoints. Results: The robust T2W texture and T2 map first-order features demonstrated the greatest sensitivity and reproducibility in capturing temporal peritumoral brain zone changes, distinguishing between time points used by K-mean. Conclusions: Delta radiomics offers added value over static analysis for early monitoring of peritumoral brain zone changes. The first-order and texture features of radiomics could serve as robust biomarkers of peritumoral invasion. These findings highlight the potential of longitudinal MRI-based radiomics to characterize glioblastoma progression and inform translational research.