Assessment and prediction of glioblastoma therapy response: challenges and opportunities. Brain 2023 Apr 19;146(4):1281-1298
Date
11/30/2022Pubmed ID
36445396Pubmed Central ID
PMC10319779DOI
10.1093/brain/awac450Scopus ID
2-s2.0-85152389552 (requires institutional sign-in at Scopus site) 4 CitationsAbstract
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.
Author List
Qi D, Li J, Quarles CC, Fonkem E, Wu EAuthor
Ekokobe Fonkem DO Chair, Professor in the Neurology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
BiomarkersDisease Progression
Glioblastoma
Humans
Machine Learning
