Identification of non-invasive biomarkers associated with glioma hemorrhage

Gliomas are among the most prevalent primary intracranial malignancies, and their management remains complex due to high rates of morbidity and mortality. Hemorrhage within gliomas, although relatively uncommon, can exacerbate disease progression, complicate clinical decision-making, and worsen patient outcomes. Identifying reliable, non-invasive biomarkers that predict or detect hemorrhage risk in glioma is an emerging research priority. This review examines the current landscape of non-invasive biomarkers—including advanced neuroimaging parameters, circulating molecular signatures, and genetic markers—that have shown potential in identifying glioma hemorrhage. We begin by summarizing the mechanisms and risk factors underlying hemorrhagic events in glioma, emphasizing the pathophysiological interplay between tumor angiogenesis, microvascular fragility, and coagulopathies. We then explore the role of modern neuroimaging techniques, such as susceptibility-weighted imaging (SWI), dynamic contrast-enhanced MRI, and radiomic-based analytical models, in uncovering hemodynamic and structural features predictive of hemorrhage. Concurrently, we evaluate key circulating biomarkers—ranging from cell-free DNA to microRNAs and extracellular vesicles—that hold promise for real-time, minimally invasive assessment of vascular integrity. By synthesizing evidence from these domains, we highlight their individual strengths and limitations and propose an integrative strategy that combines imaging and liquid biopsy approaches for enhanced diagnostic accuracy. Collectively, this review aims to provide clinicians and researchers with a comprehensive understanding of the utility and prospects of non-invasive biomarkers in the context of glioma hemorrhage, ultimately guiding more personalized surveillance and therapeutic interventions.

Authors

Institutions

Publication Details

Journal
Brain Hemorrhages
Published
2025-06-09
DOI
https://doi.org/10.1016/j.hest.2025.05.001
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identification of non-invasive biomarkers associated with glioma hemorrhage

Huaizhang Shi, В. Н. Павлов, Ozal Beylerli, Leili Gulieva
Brain Hemorrhages
Radiomics and Machine Learning in Medical Imaging
article

Identification of non-invasive biomarkers associated with glioma hemorrhage

Huaizhang Shi, В. Н. Павлов, Ozal Beylerli, Leili Gulieva
article en

Abstract

Gliomas are among the most prevalent primary intracranial malignancies, and their management remains complex due to high rates of morbidity and mortality. Hemorrhage within gliomas, although relatively uncommon, can exacerbate disease progression, complicate clinical decision-making, and worsen patient outcomes. Identifying reliable, non-invasive biomarkers that predict or detect hemorrhage risk in glioma is an emerging research priority. This review examines the current landscape of non-invasive biomarkers—including advanced neuroimaging parameters, circulating molecular signatures, and genetic markers—that have shown potential in identifying glioma hemorrhage. We begin by summarizing the mechanisms and risk factors underlying hemorrhagic events in glioma, emphasizing the pathophysiological interplay between tumor angiogenesis, microvascular fragility, and coagulopathies. We then explore the role of modern neuroimaging techniques, such as susceptibility-weighted imaging (SWI), dynamic contrast-enhanced MRI, and radiomic-based analytical models, in uncovering hemodynamic and structural features predictive of hemorrhage. Concurrently, we evaluate key circulating biomarkers—ranging from cell-free DNA to microRNAs and extracellular vesicles—that hold promise for real-time, minimally invasive assessment of vascular integrity. By synthesizing evidence from these domains, we highlight their individual strengths and limitations and propose an integrative strategy that combines imaging and liquid biopsy approaches for enhanced diagnostic accuracy. Collectively, this review aims to provide clinicians and researchers with a comprehensive understanding of the utility and prospects of non-invasive biomarkers in the context of glioma hemorrhage, ultimately guiding more personalized surveillance and therapeutic interventions.

Brain HemorrhagesVol. 6(4)
Harbin Medical University (CN), Bashkir State Medical University (RU), First Affiliated Hospital of Harbin Medical University (CN)
Zero hunger
Openalex Percentile: Top 80%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.