Eric Hexem , Taha Abd-ElSalam Ashraf Taha , Yaseen Dhemesh , Mohammad Aneel Baqar , Ayman Nada
{"title":"解密胶质母细胞瘤:揭示预测 MGMT 启动子甲基化状态的成像标记。","authors":"Eric Hexem , Taha Abd-ElSalam Ashraf Taha , Yaseen Dhemesh , Mohammad Aneel Baqar , Ayman Nada","doi":"10.1016/j.currproblcancer.2024.101156","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55193,"journal":{"name":"Current Problems in Cancer","volume":"54 ","pages":"Article 101156"},"PeriodicalIF":2.5000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deciphering glioblastoma: Unveiling imaging markers for predicting MGMT promoter methylation status\",\"authors\":\"Eric Hexem , Taha Abd-ElSalam Ashraf Taha , Yaseen Dhemesh , Mohammad Aneel Baqar , Ayman Nada\",\"doi\":\"10.1016/j.currproblcancer.2024.101156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55193,\"journal\":{\"name\":\"Current Problems in Cancer\",\"volume\":\"54 \",\"pages\":\"Article 101156\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Problems in Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0147027224000977\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Problems in Cancer","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0147027224000977","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Deciphering glioblastoma: Unveiling imaging markers for predicting MGMT promoter methylation status
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.
期刊介绍:
Current Problems in Cancer seeks to promote and disseminate innovative, transformative, and impactful data on patient-oriented cancer research and clinical care. Specifically, the journal''s scope is focused on reporting the results of well-designed cancer studies that influence/alter practice or identify new directions in clinical cancer research. These studies can include novel therapeutic approaches, new strategies for early diagnosis, cancer clinical trials, and supportive care, among others. Papers that focus solely on laboratory-based or basic science research are discouraged. The journal''s format also allows, on occasion, for a multi-faceted overview of a single topic via a curated selection of review articles, while also offering articles that present dynamic material that influences the oncology field.