A new MRI-based feature for quantifying the Diffuse Low-Grade Glioma brain infiltration and discriminating patterns of patients
Abstract
Gliomas are the most common type of primary brain tumors. Diffuse low-grade gliomas (DLGGs) are slow-growing tumors that are often minimally symptomatic for a long period of time. They progress to a higher grade, resulting in the patient's death. Treatments include surgery, chemotherapy and radiation therapy to control tumor progression. Responses to treatments are highly variable among patients. Furthermore, the diffuse component of this tumor entity has been observed but is not yet well measured. Here, we propose a new variable to quantify glioma morphology, called ESVR (Extra Sphere Volume Ratio). We use then a machine learning approach to study the importance of different variables for discriminating patterns of patients: patient-specific variables, genetic alterations of tumor tissue, and some image-based variables like ESVR and the volume of the tumor, obtained by MRI analysis. Our machine learning approach shows that the patient's age and ESVR at diagnosis, as well as the pathology results seem to play an important role. Taking the highlighted variables into account could thus help to model the tumor behavior. In association with other classical biomarkers, our new ESVR feature could help the clinician to manage the treatment, as it carries significant information in terms of aggressivity of the DLGG.
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BibTeX
@misc{moureaux2026mribased,
title = {{A new MRI-based feature for quantifying the Diffuse Low-Grade Glioma brain infiltration and discriminating patterns of patients}},
author = {Jean-Marie Moureaux and Sophie Wantz-Mézières and Cyril Brzenczek and Marie Blonski and Luc Taillandier},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.06398},
eprint = {2610.06398},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.06398},
}
RIS
TY - UNPB
TI - A new MRI-based feature for quantifying the Diffuse Low-Grade Glioma brain infiltration and discriminating patterns of patients
AU - Jean-Marie Moureaux
AU - Sophie Wantz-M{é}zi{è}res
AU - Cyril Brzenczek
AU - Marie Blonski
AU - Luc Taillandier
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.06398
UR - https://arxiv.org/abs/2610.06398
AB - Gliomas are the most common type of primary brain tumors. Diffuse low-grade gliomas (DLGGs) are slow-growing tumors that are often minimally symptomatic for a long period of time. They progress to a higher grade, resulting in the patient's death. Treatments include surgery, chemotherapy and radiation therapy to control tumor progression. Responses to treatments are highly variable among patients. Furthermore, the diffuse component of this tumor entity has been observed but is not yet well measured. Here, we propose a new variable to quantify glioma morphology, called ESVR (Extra Sphere Volume Ratio). We use then a machine learning approach to study the importance of different variables for discriminating patterns of patients: patient-specific variables, genetic alterations of tumor tissue, and some image-based variables like ESVR and the volume of the tumor, obtained by MRI analysis. Our machine learning approach shows that the patient's age and ESVR at diagnosis, as well as the pathology results seem to play an important role. Taking the highlighted variables into account could thus help to model the tumor behavior. In association with other classical biomarkers, our new ESVR feature could help the clinician to manage the treatment, as it carries significant information in terms of aggressivity of the DLGG.
ER -