P07.03.A THE IMPACT OF MOLECULAR MARKERS IN GLIOMA SURGERY

Abstract BACKGROUND Extent of resection (EOR) is a well-established prognostic factor in patients with gliomas. However, most data on overall survival (OS) following gross total resection (GTR) or subtotal resection (STR) do not account for the recent molecular classification of gliomas. MATERIAL AND METHODS We conducted a systematic review, according to PRISMA guidelines, of the PubMed database to evaluate the impact of EOR on OS in glioma patients, categorized by molecular subgroups, including 1p/19q codeleted, IDH status and MGMT promoter methylation status. Titles, abstracts and full-text papers were screened by 2 reviewers indipendentely. At both stages, differing options were discussed until agreement was reached. Only papares in english were included in the review. RESULTS A total of 295 articles were identified, of which 44 underwent full-text screening. 23 studies met inclusion criteria and were analyzed according to molecular markers. Nine studies focused on EOR in 1p/19q codeleted gliomas, showing either no significant difference or only marginal improvements in OS compared to non-codeleted IDH mutant gliomas. Five studies addressed IDH mutation status and EOR, and seven studies examined the relationship between EOR and MGMT methylation. The heterogeneity in study design and small sample sizes limited the strength of conclusions. CONCLUSION Our systematic review highlights a lack of high-quality evidence on the impact of EOR across molecularly defined glioma subtypes. The available data suggest limited or no OS benefit from GTR compared to STR in patients with 1p/19q codeleted gliomas. Further large-scale, prospective studies are needed to clarify the prognostic role of EOR in the context of molecular subtypes.

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Publication Details

Journal
Neuro-Oncology
Published
2025-10-01
DOI
https://doi.org/10.1093/neuonc/noaf193.249
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

P07.03.A THE IMPACT OF MOLECULAR MARKERS IN GLIOMA SURGERY

Leandro Antônio Gritti, Stephen John Price
Neuro-Oncology
Radiomics and Machine Learning in Medical Imaging
article

P07.03.A THE IMPACT OF MOLECULAR MARKERS IN GLIOMA SURGERY

Leandro Antônio Gritti, Stephen John Price
article en

Abstract

Abstract BACKGROUND Extent of resection (EOR) is a well-established prognostic factor in patients with gliomas. However, most data on overall survival (OS) following gross total resection (GTR) or subtotal resection (STR) do not account for the recent molecular classification of gliomas. MATERIAL AND METHODS We conducted a systematic review, according to PRISMA guidelines, of the PubMed database to evaluate the impact of EOR on OS in glioma patients, categorized by molecular subgroups, including 1p/19q codeleted, IDH status and MGMT promoter methylation status. Titles, abstracts and full-text papers were screened by 2 reviewers indipendentely. At both stages, differing options were discussed until agreement was reached. Only papares in english were included in the review. RESULTS A total of 295 articles were identified, of which 44 underwent full-text screening. 23 studies met inclusion criteria and were analyzed according to molecular markers. Nine studies focused on EOR in 1p/19q codeleted gliomas, showing either no significant difference or only marginal improvements in OS compared to non-codeleted IDH mutant gliomas. Five studies addressed IDH mutation status and EOR, and seven studies examined the relationship between EOR and MGMT methylation. The heterogeneity in study design and small sample sizes limited the strength of conclusions. CONCLUSION Our systematic review highlights a lack of high-quality evidence on the impact of EOR across molecularly defined glioma subtypes. The available data suggest limited or no OS benefit from GTR compared to STR in patients with 1p/19q codeleted gliomas. Further large-scale, prospective studies are needed to clarify the prognostic role of EOR in the context of molecular subtypes.

Neuro-OncologyVol. 27(Supplement_3)
University of Cambridge (GB), Brescia University (US), Cambridge School (PT), University of Brescia (IT)
Openalex Percentile: Top 59%
Radiomics and Machine Learning in Medical Imaging
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