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This research aims to evaluate the complementarity of PET and MR imaging for glioblastoma recurrence and improve segmentation methods.

July 22, 2026Cancer Imaging0 citationsOpen Access

Multimodal PET–MR segmentation for glioblastoma: complementarity for treatment planning and recurrence definition

AMAlejandro Mora-Rubio DKDejan Kuhn CBCarolina Bravo-Vergara

Key Points

This research aims to evaluate the complementarity of PET and MR imaging for glioblastoma recurrence and improve segmentation methods.
Utilized automatic segmentation models on data from 1,610 patients across 33 institutions.
Evaluated model performance using Dice-Sørensen-Coefficient (DSC) and statistical analysis (WSRT).
Analyzed radiomic features to classify MR-Enhancing subregions based on overlap with PET.
Achieved DSC for MR-Enhancing = 0.76 ± 0.24 and DSC for MR-Edema = 0.69 ± 0.23.
Manual delineations of recurrence showed significant differences in size (p = 0.0497) and location (DSC = 0.45 ± 0.20).
None of the MR-Enhancing based radiomic features could accurately identify PET findings (positive-predictive-value < 0.6).

Abstract

In patients with Glioblastoma (GBM), Magnetic Resonance (MR) is used for tumour diagnosis and treatment planning. Positron Emission Tomography (PET) with O-(2)-18 F-Fluoroethyl-L-Tyrosine (FET) has been recommended to distinguish local recurrence from radiogenic alterations. However, clinical practice remains hindered by the time and expertise required for tumour and organs-at-risk (OARs) segmentation and the limited evidence of the added value of PET and its restricted availability across clinical centres. This study presents automatic segmentation models and a comprehensive evaluation of PET/MR complementary biological information for recurrent disease definition. The nnU-Net was employed for segmentation using manually defined contours on 1,610 patients from 33 institutions. Model performance was evaluated by Dice-Sørensen-Coefficient (DSC). PET/MR recurrence complementarity was evaluated in 185 patients by Wilcoxon-Signed-Rank test (WSRT), DSC and radiomic features (RF). In RF analysis, MR-Enhancing subregions were classified as MR∩PET or MR-Only, based on overlap with PET. For MR-RF showing significant MR∩PET/MR-Only differences (WSRT), discrimination was further assessed by classifying RF for 3 × 3 × 3-voxel subregions within MR-Enhancing in two volumes (greater/less than the RF cohort median) and evaluating Positive-Predictive-Value and Sensitivity with MR∩PET/MR-Only. Models’ performance in the test set resulted in DSC(MR-Enhancing) = 0.76 ± 0.24, DSC(MR-Edema) = 0.69 ± 0.23, DSC(PET-Uptake) = 0.71 ± 0.20, DSC(Planning-Target-Volume) = 0.93 ± 0.05, DSC(OARs) = 0.70 ± 0.13. Manual MR and FET-PET based GBM recurrence delineations differed significantly in size ( p = 0.0497) and location (DSC = 0.45 ± 0.20). From the 37 MR-RF showing significant differences between MR∩PET and MR-Only ( p < 0.05), none of the MR-Enhancing based RF-maps allowed spatial identification of PET findings (positive-predictive-value and sensitivity < 0.6). The resulted segmentation models could facilitate PET/MR integration in GBM treatment. PET/MR comparison supports the complementarity of FET-PET.

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Cite This Study

Mora-Rubio et al. (Mon,) studied this question.

synapsesocial.com/papers/6a605d1d4163e025518d7395 https://doi.org/https://doi.org/10.1186/s40644-026-01086-w

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