Related Experiment Video
Updated: May 4, 2026
Translational Orthotopic Models of Glioblastoma Multiforme
Published on: February 17, 2023
Recurrent glioblastoma multiforme versus radiation injury: a multiparametric 3-T MR approach
Alfonso Di Costanzo1, Tommaso Scarabino, Francesca Trojsi
1Dipartimento di Medicina e Scienze per la Salute, Università del Molise, Via De Sanctis snc, 86100, Campobasso, Italy, alfonso.dicostanzo@unimol.it.
Objective:
The discrimination between recurrent glioma and radiation injury is often a challenge on conventional magnetic resonance imaging (MRI). We verified whether adding and combining proton MR spectroscopic imaging ((1)H-MRSI), diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) information at 3 Tesla facilitate such discrimination.
Materials And Methods:
Twenty-nine patients with histologically verified high-grade gliomas, who had undergone surgical resection and radiotherapy, and had developed new contrast-enhancing lesions close to the treated tumour, underwent MRI, (1)H-MRSI, DWI and PWI at regular time intervals. The metabolite ratios choline (Cho)/normal( n )Cho n , N-acetylaspartate (NAA)/NAA n , creatine (Cr)/Cr n , lactate/lipids (LL)/LL n , Cho/Cr n , NAA/Cr n , Cho/NAA, NAA/Cr and Cho/Cr were derived from (1)H-MRSI; the apparent diffusion coefficient (ADC) from DWI; and the relative cerebral blood volume (rCBV) from PWI.
Results:
In serial MRI, recurrent gliomas showed a progressive enlargement, and radiation injuries showed regression or no modification. Discriminant analysis showed that discrimination accuracy was 79.3 % when considering only the metabolite ratios (predictor, Cho/Cr n ), 86.2 % when considering ratios and ADC (predictors, Cho/Cr n and ADC), 89.7 % when considering ratios and rCBV (predictors, Cho/Cr n , Cho/Cr and rCBV), and 96.6 % when considering ratios, ADC and rCBV (predictors, Cho/Cho n , ADC and rCBV).
Conclusions:
The multiparametric 3-T MR assessment based on (1)H-MRSI, DWI and PWI in addition to MRI is a useful tool to discriminate tumour recurrence/progression from radiation effects.
Insights
Multiparametric MRI, combining proton MR spectroscopic imaging, diffusion-weighted imaging, and perfusion-weighted imaging, accurately distinguishes recurrent glioma from radiation injury. This advanced imaging approach improves diagnostic confidence in neuro-oncology.
Area of Science:
- Neuroimaging
- Oncology
- Radiology
Background:
- Differentiating recurrent glioma from radiation injury post-treatment is challenging with conventional MRI.
- Advanced MRI techniques are needed to improve diagnostic accuracy.
Purpose of the Study:
- To evaluate the utility of combining proton MR spectroscopic imaging ((1)H-MRSI), diffusion-weighted imaging (DWI), and perfusion-weighted imaging (PWI) at 3 Tesla for discriminating recurrent glioma from radiation injury.
- To assess the diagnostic performance of multiparametric MRI in neuro-oncology follow-up.
Main Methods:
- Twenty-nine patients with high-grade gliomas underwent serial MRI, (1)H-MRSI, DWI, and PWI.
- Metabolite ratios, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV) were derived.
- Discriminant analysis was used to assess discrimination accuracy.
Main Results:
- Multiparametric MRI achieved a high discrimination accuracy of 96.6% when combining metabolite ratios, ADC, and rCBV.
- Individual parameters showed varying degrees of accuracy, with metabolite ratios (Cho/Cr n) alone achieving 79.3% accuracy.
- The combination of imaging modalities significantly improved diagnostic performance compared to conventional MRI or single advanced techniques.
Conclusions:
- Multiparametric 3-Tesla MRI, integrating (1)H-MRSI, DWI, and PWI, is a valuable tool for differentiating tumor recurrence/progression from radiation effects.
- This advanced imaging approach enhances diagnostic capabilities in managing patients with treated gliomas.
- The findings support the clinical utility of advanced MRI protocols in neuro-oncology.
