TY - JOUR
T1 - Deciphering glioblastoma
T2 - Unveiling imaging markers for predicting MGMT promoter methylation status
AU - Hexem, Eric
AU - Taha, Taha Abd El Salam Ashraf
AU - Dhemesh, Yaseen
AU - Baqar, Mohammad Aneel
AU - Nada, Ayman
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2025/2
Y1 - 2025/2
N2 - Glioblastoma, the most common primary malignant tumor of the central nervous system in adults, is also among the most lethal. Despite a comprehensive treatment approach which utilizes surgery and postoperative chemoradiation, prognosis typically remains dismal. However certain epigenetic modifications, such as methylation of the MGMT promoter, have been proven to correlate with improved post-treatment outcomes. The 2021 WHO classification emphasizes molecular characteristics, highlighting shared genomic alterations across different grades and positioning MGMT methylation as a key influencer of outcomes. A combined diagnostic approach involving current imaging technology and emerging radiomics and deep learning models may allow for timely and accurate prediction of MGMT methylation status and therefore earlier and more individualized treatment and prognostication. Though these advanced radiomics models are rapidly emerging, additional development, standardization, and implementation may lead to a higher and more individualized level of patient care. This review explores the potential of imaging features in predicting MGMT promoter methylation, a critical determinant of therapeutic response and patient outcomes.
AB - Glioblastoma, the most common primary malignant tumor of the central nervous system in adults, is also among the most lethal. Despite a comprehensive treatment approach which utilizes surgery and postoperative chemoradiation, prognosis typically remains dismal. However certain epigenetic modifications, such as methylation of the MGMT promoter, have been proven to correlate with improved post-treatment outcomes. The 2021 WHO classification emphasizes molecular characteristics, highlighting shared genomic alterations across different grades and positioning MGMT methylation as a key influencer of outcomes. A combined diagnostic approach involving current imaging technology and emerging radiomics and deep learning models may allow for timely and accurate prediction of MGMT methylation status and therefore earlier and more individualized treatment and prognostication. Though these advanced radiomics models are rapidly emerging, additional development, standardization, and implementation may lead to a higher and more individualized level of patient care. This review explores the potential of imaging features in predicting MGMT promoter methylation, a critical determinant of therapeutic response and patient outcomes.
KW - Epigenetic
KW - Glioblastoma
KW - MGMT promoter
KW - Prognosis
UR - http://www.scopus.com/inward/record.url?scp=85208592414&partnerID=8YFLogxK
U2 - 10.1016/j.currproblcancer.2024.101156
DO - 10.1016/j.currproblcancer.2024.101156
M3 - Review article
C2 - 39531875
AN - SCOPUS:85208592414
SN - 0147-0272
VL - 54
JO - Current Problems in Cancer
JF - Current Problems in Cancer
M1 - 101156
ER -