ArticleCancers2026
PET|MR Open Source Radiomics Models for Treatment Outcome Prediction in Recurrent GBM.
Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
BACKGROUND/
objectivesGlioblastoma (GBM) is a hard-to-treat cancer with a 5-year survival rate of 5.7% without significant improvement in the past decades. Personalised strategies based on improved tumour characterisation and treatment-outcome prediction could support treatment adaptation and improve therapeutic efficacy. Magnetic Resonance (MR) is the standard imaging modality for GBM radiotherapy (RT) planning. Positron Emission Tomography (PET) with O-(2)-[^18F]fluoroethyl-L-tyrosine (FET) is recommended for distinguishing recurrence from pseudo-progression. However, evidence on PET|MR complementarity for predicting treatment outcome in recurrent GBM remains limited.
methodsWe evaluated FET-PET|MR biomarkers to predict Time-To-Progression (TTP), Overall Survival (OS) and Acute Recurrence (AR) in a prospective cohort of 185 recurrent GBM patients from 15 institutions. T1-weighted contrast-enhanced, Fluid-Attenuation Inversion-Recovery, Apparent-Diffusion-Coefficient maps and FET-PET images were analysed. Gross- and Planning-Target Volumes (GTV/PTV) were manually delineated and the intersection of PET|MR-GTVs was defined as MR∩PET. Radiomics models were developed (5-fold cross-validation) and evaluated in a held-out test set. Additionally, nnUNet was used to predict MR∩PET from MR sequences.
resultsMR∩PET volume yielded the highest number of statistically significant models, 15 versus 6 for PET|MR-GTVs and 0 for PET|MR-PTVs. Of these 21 models, 19 required the inclusion of PET imaging. The best-performing models discriminated between short and long OS, with
conclusionsThe PET|MR radiomic models derived from our multicentre prospective cohort support the identification of recurrent GBM patients with poor RT outcomes, potentially enabling future personalised strategies for treatment improvement.
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