Imaging-Guided Re-irradiation and Adaptive Radiotherapy for Recurrent Glioblastoma

Recurrent glioblastoma (rGBM) remains one of the hardest problems in neuro-oncology. At relapse, radiotherapy decisions are often limited by uncertain target definition, prior dose exposure, and narrow therapeutic margins between tumor control and neurotoxicity. In parallel, imaging capabilities have advanced rapidly: multiparametric MRI, amino-acid PET, deformable registration, and radiomics now offer practical ways to define biologically active disease, refine target volumes, and support safer re-irradiation strategies. Yet, many imaging advances still fail to translate into consistent radiotherapy actions at the point of care.This Research Topic focuses on imaging that directly informs radiotherapy planning and delivery for rGBM, with an emphasis on re-irradiation and adaptive workflows. The goal is to move from promising imaging signals to reproducible decisions on patient selection, target definition, and dose strategy in previously irradiated patients. By bringing together radiation oncology, neuroradiology, nuclear medicine, physics, and data science, this collection aims to highlight validated methods and practical workflows that improve precision and safety in rGBM radiotherapy.To achieve this goal, we welcome submissions addressing (but not limited to) the following themes:• Imaging-to-contour translation for re-irradiation such as multiparametric MRI (diffusion, perfusion, spectroscopy), PET-informed delineation, biologic target volume (BTV) concepts, integration into GTV/CTV/PTV strategies, and approaches to reduce inter-observer variability.• PET-guided radiotherapy in rGBM amino-acid PET for target definition, eligibility, and risk stratification, practical co-registration and workflow implementation, and PET-informed dose painting concepts.• Adaptive and MR-guided re-irradiation workflows in MR-linac/online adaptation, margin strategies, setup/geometry considerations, and decision rules for adapting targets or plans during treatment.• Dose accumulation and feasibility after prior RT, deformable image registration for cumulative dose estimation, organs-at-risk constraint handling, and toxicity-aware planning strategies.• Imaging-linked dose concepts and optimization for escalation to imaging-defined subvolumes, voxel-based approaches, robustness considerations, and clinically implementable planning methods.• Patterns-of-failure and dose–response mapping in recurrence mapping relative to delivered dose and imaging-defined targets to inform future contouring and dose strategies.• Imaging to enable safe re-irradiation, distinguishing recurrence vs treatment effect to support re-irradiation selection/targeting, and imaging approaches to predict or mitigate radionecrosis and neurocognitive toxicity.• Harmonized acquisition/segmentation, reporting standards, multicenter reproducibility, external validation, and benchmark datasets relevant to radiotherapy use cases.We encourage Original Research, Systematic Reviews, Methods/Protocol papers, Clinical workflow/implementation studies, and Brief Research Reports that connect imaging methods to radiotherapy planning, adaptation, dose strategy, or toxicity-aware decision-making in recurrent glioblastoma (and, where appropriate, recurrent high-grade glioma cohorts where rGBM analyses are clearly identifiable).Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Elvira V Grigorieva, Clark Chen, Weisi Yan Source: https://www.frontiersin.org/research-topics/81914/imaging-guided-re-irradiation-and-adaptive-radiotherapy-for-recurrent-glioblastoma