Assessment and Prediction of Glioblastoma Therapy Response: Challenges and Opportunities

Authors

Dan Qi
Jing Li
C Chad Quarles
Ekokobe Fonkem
Erxi Wu

Language

English

Publication Date

4-19-2023

Journal

Brain

DOI

10.1093/brain/awac450

PMID

36445396

PMCID

PMC10319779

PubMedCentral® Posted Date

11-29-2022

PubMedCentral® Full Text Version

Post-print

Abstract

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is < 10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Keywords

Glioblastoma, Humans, Disease Progression, Biomarkers, Machine Learning, Clinical Decision Rules, glioblastoma, therapy response, liquid biomarker, multiparametric imaging, machine learning

Published Open-Access

yes

Recommended Citation

Qi, Dan; Li, Jing; Quarles, C Chad; et al., "Assessment and Prediction of Glioblastoma Therapy Response: Challenges and Opportunities" (2023). Faculty, Staff and Student Publications. 6450.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6450