Advances in Radiation Treatment for Brain Tumors

A special issue of Current Oncology (ISSN 1718-7729). This special issue belongs to the section "Neuro-Oncology".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 464

Special Issue Editor


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Guest Editor
Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
Interests: radiation therapy; patient outcomes

Special Issue Information

Dear Colleagues,

While significant technological advancements have become the norm in radiation oncology, these have not always been accompanied by observable improvement in patient outcomes and have required significant resource-intense approaches to clinical implementation and testing to justify rising costs. Areas of ongoing clinical need can be addressed in a multifactorial fashion via several critical need areas comprising: (1)  biologically personalized treatment planning including integration of multi-omic data to tailor dose distribution to tumor biology and adjustment of treatment fields based on tumor response; (2) advanced AI driven automation for efficiency and precision including auto contouring, auto segmentation and AI based quality assurance; (3) the combination of AI with advanced treatment modalities including FLASH and heavy ion radiotherapy; (4)  leveraging radiotherapy immune modulation and (5) actively focusing on big data and federated learning initiatives that incorporate radiation therapy dosimetry data and train AI algorithms across multiple institutions including large-scale AI models analyzing clinical, imaging and molecular data to improve prognostication and decision support. 

Dr. Andra Krauze
Guest Editor

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Keywords

  • personalized treatment
  • artificial intelligence
  • automation
  • segmentation
  • adaptive treatment
  • immune modulation
  • fed-erated learning
  • multi-omics

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Published Papers (1 paper)

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Research

23 pages, 4385 KB  
Article
Serum p-Cresol and 7-HOCA Levels and Fatty Acid and Purine Metabolism Are Associated with Survival, Progression, and Molecular Classification in GB—Serum Proteome and Metabolome Analysis Pre vs. Post Up-Front Chemoirradiation
by Andra V. Krauze, M. Li, Y. Zhao, E. Tasci, S. Chappidi, T. Cooley Zgela, M. Sproull, M. Mackey and K. Camphausen
Curr. Oncol. 2025, 32(11), 650; https://doi.org/10.3390/curroncol32110650 - 20 Nov 2025
Viewed by 138
Abstract
Background: Glioblastoma (GB) is the most common primary brain tumor, with poor prognosis, significant neurological symptoms, and near-universal recurrence. Biomarker development is often limited by the scarcity of tumor tissue available for study. Noninvasive serum-based profiling offers potential to improve outcomes. Purpose: This [...] Read more.
Background: Glioblastoma (GB) is the most common primary brain tumor, with poor prognosis, significant neurological symptoms, and near-universal recurrence. Biomarker development is often limited by the scarcity of tumor tissue available for study. Noninvasive serum-based profiling offers potential to improve outcomes. Purpose: This study examined serum proteomic and metabolomic profiles pre- and post-concurrent chemoirradiation (CRT) to identify associations with patient outcomes and molecular classification, and to explore relevant signaling and metabolic pathways. Methods: Serum samples from 109 GB patients, obtained prior to and following completion of CRT, were analyzed with each patient serving as their own control, using a SOMAScan® proteomic assay (7289 proteins) and metabolomics (SECIM, 6015 compounds). Clinical data were obtained through chart review. Proteomic and metabolomic changes were examined at baseline (prior to CRT) and in alteration (pre- vs. post-CRT) for their association with overall survival (OS), progression-free survival (PFS), MGMT, and IDH status. Cox models, gene set enrichment analysis (Hallmark, GSEA), and Kaplan–Meier survival analysis were used. Results: Several hundred proteins and metabolites were associated with OS and PFS. MGMT status was known in 60% and IDH in 38% of patients. Pre-CRT DLST (HR 11.7, p < 0.001, adj p = 0.01) was the only protein significantly associated with OS. Pre-CRT, and higher 7-HOCA was linked to worse OS (HR 1.3) and PFS (HR 1.5), while increased p-cresol was associated with improved OS (HR 0.8) and PFS (HR 0.9). Kaplan–Meier analysis based on signal alteration post-CRT vs. pre-CRT, revealed superior OS with lower DLST and MSR1 and superior PFS with higher PGAM2 and ATG5, and lower 7-HOCA. Pathway analysis linked improved PFS to fatty acid metabolism, citric acid cycle, and purine biosynthesis. MGMT and IDH class comparisons revealed associations primarily with amino acid and fatty acid metabolism. Both MGMT methylation and IDH mutation correlated with increased PLAG12B expression, with significance only for MGMT (p < 0.001). IDH mutation was associated with decreased MSR1 (p = 0.047) and p-cresol (p < 0.001). Conclusions: Serum-based fatty acid and purine metabolism pathways are associated with OS and PFS in GB. 7-HOCA and p-cresol emerged as potential biomarkers linked to treatment response and molecular subtype. These findings support further investigation of noninvasive biospecimens for clinically actionable biomarkers in GB. Full article
(This article belongs to the Special Issue Advances in Radiation Treatment for Brain Tumors)
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